A SEMINAR REPORT ON
INTELLIGENT WASTE CLASSIFICATION
Submitted in partial fulfilment of the requirement
for the award of the degree of
BACHELOR OF TECHNOLOGY
In
COMPUTER SCIENCE AND ENGINEERING
Submitted by
P. KRISHNA PANCHAJANYAM – 20841A05C0
DEPARTMENT OF COMPUTER SCIENCE ENGINEERING
AURORA’S TECHNOLOGICAL AND RESEARCH INSTITUTE
Approved by AICTE and Affiliated to JNTUH Accredited by NAAC with ‘A’Grade
Parvathapur, Uppal, Medipally (M), Medchal (D). Hyderabad – 500098
NOVEMBER, 2023
AURORA’S TECHNOLOGICAL AND RESEARCH INSTITUE
Approved by AICTE and Affiliated to JNTUH Accredited by NAAC with ‘A’ Grade
Parvathapur, Uppal, Medipally (M), Medchal (D).Hyderabad – 500098
DEPARTMENT OF COMPUTER SCIENCE ENGINEERING
CERTIFICATE
Certified that seminar work entitled “INTELLIGENT WASTE CLASSIFICATION”
is a bonafide work carried out in the fourth year by PERAVALI KRISHNA
PANCHAJANYAM (20841A05C0) in partial fulfilment for the award of degree of
Bachelor of Technology in Computer Science Engineering from JNTU Hyderabad during
the academic year 2023-2024.
Ms. V. Shilpa
Ms. A. Gowthami
Ms. A. Durga Pavani
Guide
Coordinator
HOD
Associate Professor
Assistant Professor
Department of CSE
Department of CSE
Department of CSE
II
ACKNOWLEDGEMENT
I would like to express my gratitude to Ms. V. Shilpa, Technical Seminar Guide,
Associate Professor for her guidance, valuable suggestions and encouragement in
completing the Technical Seminar work within the stipulated time.
I would like to express my gratitude to Ms. A. Gowthami, Technical Seminar
Coordinator, Assistant Professor for her guidance, valuable suggestions and
encouragement in completing the Technical Seminar work within the stipulated time.
I would like to express my immense gratitude to Ms. A. Durga Pavani, Head of
the Department CSE, ATRI, Associate Professor for her guidance, valuable suggestions
and encouragement in completing the Technical Seminar work within the stipulated time.
I would like to express my sincere thanks to Dr. A. Mahesh Babu, Principal,
Aurora’s Technological and Research Institute, Parvathapur, Hyderabad – 500098 for
permitting me to do my Technical Seminar work.
Finally, I would also like to thank the people who have directly or indirectly
helped me, my parents and my friends for their cooperation in completing the Technical
Seminar work.
P. KRISHNA PANCHAJANYAM
20841A05C0
III
ABSTRACT
Globally waste is being increased a lot. Traditional waste classification methods
rely heavily on manual classification, which is labour intensive, error-prone, and often
inefficient. This report explores the transformative potential of intelligent waste
classification system in addressing these issues. The accumulation of solid waste in the
urban areas is becoming a great concern, and it would result in environmental pollution
and may be hazardous to human health if it is not properly managed. It is important to
have an advanced/intelligent waste management system to manage a variety of waste
materials. One of the most important steps of waste management is the separation of the
waste into the different components and this process is normally done manually by handpicking. To simplify the process, intelligent waste material classification system was
introduced, which was developed with Robotic arm for picking the waste and the Masked
Regional Convolutional neural networks(R-CNN) is a deep learning tool and serves as
the extractor, used to classify the waste into different groups/types such as glass, metal,
paper, and plastic etc.
IV
LIST OF CONTENT
ACKNOWLEDGEMENT
III
ABSTRACT
IV
LIST OF CONTENT
IV
LIST OF FIGURES
VII
LIST OF TABLES
IX
1. INTRODUCTION
1.1 Solid waste
1.1.1 Types of solid waste
1.2 Effects of solid waste
1
1
1
2
1.2.1 Effect of solid waste on environment
2
1.2.2 Effect of solid waste on living beings
3
1.3 Management of solid waste
1.3.1 Recycling of solid waste
2. WASTE CLASSIFICATION
2.1 Existing methods of waste classification
3
4
5
5
2.1.1 Hand Classification Method
5
2.1.2 Semi-hand classification Method
6
2.2 Industrial waste classification methods
6
2.2.1 Wasatch Integrated Waste Management District (WIWD)
7
2.2.2 TOMRA Sorting Solutions
7
3. EVOLUTION OF DEEP LEARNING
9
3.3 Convolutional Neural Network (CNN)
10
V
3.3.1 Convolutional layer
11
3.3.2 Pooling Layer
12
3.4 Fast Regional Convolutional Neural Networks (Fast R-CNN)
13
3.5 Regional Convolutional Neural networks
14
3.6 Masked Regional Convolutional Neural Networks
16
3.7 Residual Network-50 (ResNet-50)
17
3.7.1 ResNet-50 Architecture
18
4. INTELLIGENT WASTE CLASSIFIER
4.1 Components
4.1.1 Waste feeder
4.1.2 Trommel
20
21
21
22
4.1.3 Conveyor belt:
22
4.1.4 Camera
23
4.1.5 Robotic arm
23
4.1.6 Vacuum gripper
24
5. DEEP LEARNING WASTE CLASSIFICATION
27
5.1 Making Dataset
27
5.1.1 Synthetic complex dataset
28
5.2 Training of Masked R-CNN Model
30
5.2.1 Training Analysis
31
5.2.2 Training results of R-CNN
32
5.2.3 Obstacles Associated With Detecting And Classifying Solid Waste
34
6. WORKING OF INTELLIGENT WASTE CLASSIFIER
36
7. ADVANTAGES & LIMITATTIONS
37
7.1 Advantages
37
VI
7.2 Limitations
37
8. CONCLUSION
39
9. REFERENCES
40
VII
LIST OF FIGURES
Figure no.
Description
Page no.
1.1
Statistics of waste generation around world
1
1.2
Solid waste types
3
3.1
Convolutional neural networks architecture
10
3.2
Basic convolution operation
11
3.3
Max pooling operation
12
3.4
Architecture of Regional Convolutional Neural networks
15
3.5
Architecture of ResNet-50
18
4.1
Intelligent waste classifier robotic arm
20
4.2
Photograph of intelligent waste classifier in working state
21
4.3
Waste Feeder
21
4.4
Trommel in industry
22
4.5
A Conveyor belt and components
23
4.6
ZED AI based camera
23
4.7
ABB IRB360 delta robot (robotic arm) for picking waste
24
4.8
Vacuum gripper
24
4.9
Two types of air flow. (a) Vacuum suction (blue arrow). (b)
25
Compressed air (red arrow) used for disposal.
VIII
5.1
The basic data set, including representations of each class of
studied material: (a) aluminum (first row), paper and
cardboard (second row), PET bottles (third row), and nylon
(fourth row). (b) The augmented data set.
27
5.2
Four samples from the training data set presented with
29
annotated masks
5.3
Material classification using Mask R-CNN.
34
IX
LIST OF TABLES
Table no.
Description
Page no.
5.1
The training parameter configuration
30
5.2
The training and validation loss
31
5.3
The classification results of R-CNN
33
X
1. INTRODUCTION
In our world waste is increasing everyday with time because of population which
results increase in demand of daily use products due to this waste too is increasing. Let’s
see in detailed manner about solid waste.
1.1 Solid waste
Solid waste refers to any type of garbage, trash, refuse or discarded material.
Figure 1.1: Statistics of waste generation around world
It can be categorized according to where the waste is generated, for example as
municipal solid waste, health care waste and e-waste. Over 2 billion tons of municipal
solid waste are produced annually. The world generates 2.01 billion tonnes of municipal
solid waste annually, with at least 33 percent of that—extremely conservatively—not
managed in an environmentally safe manner. Worldwide, waste generated per person per
day averages 0.74 kilogram but ranges widely, from 0.11 to 4.54 kilograms. Though they
only account for 16 percent of the world’s population, high-income countries generate
about 34 percent, or 683 million tonnes, of the world’s waste. The World Bank report
showed that there are almost 4 billion tons of waste around the world every year and the
urban alone contributes a lot to this number. In the next 25 years, waste accumulation in
less developed countries will increase more drastically.
1.1.1 Types of solid waste
The following are some common types of solid waste:
1) Municipal Solid Waste (MSW): This is the everyday waste generated by households
1
and businesses. It includes items like food scraps, packaging materials, clothing,
appliances and other non-hazardous materials.
2) Construction and Demolition (C&D) Waste: This category encompasses waste
produced during the construction, renovation, and demolition of buildings and
infrastructure. It includes materials like concrete, wood, drywall, and metals.
3) Electronic Waste (E-Waste): E-waste consists of discarded electronic and electrical
equipment, including old computers, mobile phones, televisions, and other electronic
devices.
4) Recyclable Waste: Materials that can be collected, processed, and reused to make new
products, such as paper, cardboard, glass, and certain plastics.
5) Green Waste: Organic waste from gardening and landscaping activities, such as grass
clippings, tree branches, and leaves.
6) Plastic Waste: plastic waste refers to the waste generated by the products which are
made with plastic such as plastic cups, PET bottles, caps etc.
1.2 Effects of solid waste
Waste is defined as unwanted and unusable materials and is regarded as a
substance which is of no use. Waste that we see in our surroundings is also known as
garbage. Garbage is mainly considered as a solid waste that includes wastes from our
houses (domestic waste), wastes from schools, offices, etc. (municipal wastes) and wastes
from industries and factories (industrial wastes).
1.2.1 Effect of solid waste on environment
Improper disposal and mismanagement of solid waste have profound and
detrimental effects on the environment. The pollution of land and soil, a consequence of
landfills and illegal dumping, can contaminate the soil and hinder natural plant growth,
diminishing biodiversity. Solid waste often leaches harmful chemicals into groundwater,
resulting in water pollution. Moreover, when waste is not handled responsibly, it can be
carried by surface runoff into rivers, lakes, and oceans, exacerbating water pollution. The
open burning of waste releases toxic air pollutants and greenhouse gases, contributing to
air pollution and climate change. Landfills, in particular, emit methane, a potent
2
greenhouse gas that adds to global warming. Wildlife and ecosystems suffer as animals
ingest or become entangled in discarded waste, especially plastics, disrupting natural
habitats and diminishing biodiversity. Furthermore, the presence of unsightly waste
negatively impacts the aesthetics of an area and can expose humans and animals to health
risks, such as respiratory issues and infections. Solid waste generation often relies on the
extraction of natural resources, contributing to resource depletion.
To mitigate these environmental impacts, it is crucial to promote responsible
waste management practices, reduce waste generation, and enforce regulations to
safeguard the environment and human health.
1.2.2 Effect of solid waste on living beings
Figure 1.2: Solid waste types
Without proper recycle of waste, especially plastic causes dangerous threat, not
only to humans but also to other living beings. Mainly cows, buffaloes and monkeys are
consuming plastic wrapped food and bags and are being died due to indigestion. Many
marine creatures too are being suffered with solid waste, many whales and other aqua
creatures are entangled with fishing nets and plastic ropes, which causes suffocation and
dying. Hence it is necessary to recycle the waste to protect the environment.
1.3 Management of solid waste
The main method of managing the waste is landfilling, which is inefficient and
3
expensive and polluting natural environment. For example, the landfill site can affect the
health of the people who stay around the landfill site. Another common way of managing
waste is burning waste and this method can cause air pollution and some hazardous
materials from the waste spread into the air which can cause cancer.
1.3.1 Recycling of solid waste
By considering 4R’s method i.e. reduce, reuse, recycle and recover, one can
manage the waste in eco-friendly manner. One of the efficient way of managing solid
waste is by recycling. So to protect environment and to reduce waste we need to recycle
the waste. In recycling, one of the important stage is classification of waste according to
their type. In very few countries various bins are used to dispose wastes according to their
type. Mostly in many countries various wastes are disposed in single bin and buried under
earth. Instead of burying under earth we can reuse the plastic by some methods to deform
the existing plastic into required shape.
4
2. WASTE CLASSIFICATION
Waste classification is a crucial aspect of modern waste management systems,
aimed at categorizing and segregating different types of waste materials based on their
characteristics, composition, and potential environmental impact. This process enables
the proper handling, disposal, and, ideally, the recycling or recovery of waste materials,
minimizing the negative effects on the environment. Waste is typically classified into
various categories, including municipal solid waste (MSW), hazardous waste, industrial
waste, electronic waste (e-waste), and more. Each category requires specific handling and
treatment methods to ensure the safety of human health and the preservation of the
environment. Waste classification is integral to sustainable waste management practices,
as it allows for the identification and prioritization of materials that can be reduced,
reused, recycled, or recovered. By differentiating between recyclable, hazardous, and
non-hazardous waste, it becomes possible to divert materials away from landfills and
incinerators, promoting resource conservation and reducing pollution. Proper waste
classification also facilitates the implementation of recycling programs and the efficient
classification of waste streams in recycling facilities. Through accurate and systematic
waste classification, communities can work towards a more responsible and
environmentally-friendly approach to waste management, which ultimately benefits both
the planet and its inhabitants.
2.1 Existing methods of waste classification
Mainly waste is classified to recycle the sorted waste or to reuse the sorted waste.
Mainly they contain plastic bottles, aluminium cans, bottle caps, paper, plastic bags, etc.
Existing methods to classify the waste is hand picking and semi hand picking, let’s them
in more detailed manner in further sections.
2.1.1 Hand Classification Method
Hand classification is a manual waste separation method where trained workers
physically categorize waste into recyclables, compostable, and non-recyclables. Waste
management relies heavily on hand classification. The advantages of hand classification
include producing high-quality recyclables and being a low-cost, easily implemented
5
solution. However, it is labour-intensive, can expose workers to hazardous materials, and
may not be cost-effective for large-scale operations. Efforts have been made in recent
years to increase the efficiency of hand classification through the use of technology, such
as handheld devices that can assist workers in identifying materials. Manual classification
still plays a crucial role in the efficient operation of solid waste management systems
because it has an influence on the efficiency and sustainability of- waste separation and
recycling processes. Basically human power is enough for classification but, due to
working in dump yards for more time, they are facing a lot of health problems like
asthma, lung cancer, allergies etc. so, if we could automate the process of classification, it
will be helpful to humans and may reduce solid waste and can recycle it and it contributes
in saving environment.
2.1.2 Semi-hand classification Method
It is a hybrid waste separation method that combines manual and mechanical
classification techniques. It involves trained workers separating waste into different
categories, but with the aid of technology such as conveyor belts, screens, and sensors.
The main advantage of semi-hand classification is that it combines the benefits of both
manual and mechanical classification methods. It can produce high-quality recyclables
and reduce the health and safety risks associated with hand classification. However, there
are several limitations to this method that should be considered, such as human error,
labour-intensiveness, health and safety risks, limited classification accuracy, and limited
scalability. Human error can lead to inconsistencies in the classification process due to
human error, labour-intensiveness, health and safety risks, limited classification accuracy,
and limited scalability. While semi-hand classification has been used effectively in waste
management for many years, it has several limitations that need to be considered. These
include human error, labour intensiveness, health and safety risks, limited classification
accuracy, and limited scalability. As a result, waste management organizations should
consider other more advanced and automated classification technologies to improve the
efficiency and accuracy of their operations.
2.2 Industrial waste classification methods
Sources of waste can be broadly classified into four types: Industrial,
6
Commercial, Domestic, and Agricultural. Recycling of waste product is very important as
this process helps in processing waste or used products into useful or new products.
Recycling helps in controlling air, water, and land pollution. It also uses less energy.
There are a number of items that can be recycled like paper, plastic, glass, etc. Recycling
helps in conserving natural resources and also helps in conserving energy. Recycling
helps in protecting the environment as it helps in reducing air, water, and soil pollution.
2.2.1 Wasatch Integrated Waste Management District (WIWD)
The Wasatch is a government-run organization that provides waste management
services for the Wasatch front in Utah. The district operates landfills, transfer stations,
and recycling centres to handle the waste of more than 800,000 residents and businesses.
WIWD employs cutting-edge technologies to reduce waste and advance ecological
practices. Among these technological advancements is the implementation of energyfrom-waste systems, which transform waste materials into usable energy. The district also
operates a composting facility that turns yard waste into a valuable soil amendment.
The district's revenue is generated primarily through the sale of landfill space and
services. WIWD also receives funding from grants and assessments of residential and
commercial properties. The district uses its revenue to maintain its facilities, implement
sustainability programs, and provide education and outreach to the community. The fiscal
resources of the district come principally from service fees and the energy sales. The
district imposes a routine charge on residential and commercial containers utilizing
automated side-load mechanisms. For the intake of all other waste categories, a dedicated
fee per unit of waste, known as a tipping fee, is implemented. In short, WIWD is a leader
in waste management and sustainability, utilizing semi-automatic cutting-edge
technology to reduce waste and generate revenue. Its commitment to sustainability and
community education makes it a model for other waste management organizations.
2.2.2 TOMRA Sorting Solutions
TOMRA is a leading provider of sensor-based sorting technology for the solid
waste management industry. The company's advanced sorting methodology is designed
to efficiently separate valuable resources from the waste stream and divert them from
landfills to be recycled or reused. TOMRA's solid waste sorting methodology is based on
7
advanced sensor technology, including near-infrared (NIR) and X-ray fluorescence
(XRF), which allows for highly accurate and efficient sorting of waste materials. The
company's sorting systems can detect and separate a wide range of materials, including
plastics, paper, metals, glass, and organic waste. The sorting process starts with the waste
being fed into the machine, where it is separated into different streams based on material
type and quality. The sensors then detect the properties of each material, such as its
density, size, and colour, and a high-speed ejector system separates the material into
different streams based on these properties. In terms of yearly income, TOMRA Sorting
Solutions is a subsidiary of the Norwegian company, TOMRA Systems ASA, which is
publicly traded on the Oslo Stock Exchange. According to its most recent financial
statements, the company generated revenue of approximately 10.1 billion Norwegian
Kroner (approximately $1.2 billion USD) in 2020, representing a year-over-year increase
of 7%. This revenue is primarily generated through the sale of sensor-based sorting
equipment. TOMRA Sorting Solutions has emerged as a prominent player in the domain
of solid waste management, offering advanced sorting technology that enables its
customers to increase recycling rates, reduce waste, and improve sustainability.
8
3. EVOLUTION OF DEEP LEARNING
The evolution of deep learning can be traced back to the 1950s, when researchers
first began to develop artificial neural networks. Neural networks are inspired by the
structure and function of the human brain, and they are able to learn complex patterns
from data. In the early days of deep learning, neural networks were very simple and could
only learn simple patterns. However, over time, researchers developed more powerful
neural network architectures and training algorithms. This led to a breakthrough in 2006,
when Geoffrey Hinton and his team showed that deep neural networks could be used to
achieve state-of-the-art results on a variety of image classification tasks. Since then, deep
learning has revolutionized many fields, including computer vision, natural language
processing, and machine translation. Deep learning models are now used in a wide range
of applications, such as self-driving cars, facial recognition, and medical diagnosis.
Here is a timeline of some of the key milestones in the evolution of deep learning:
1943: Warren McCulloch and Walter Pitts develop the McCulloch-Pitts neuron, a simple
mathematical model of a neuron.
1950: Frank Rosenblatt develops the perceptron, a single-layer neural network.
1960: David Rumelhart, Geoffrey Hinton, and Ronald Williams develop the back
propagation algorithm, which allows neural networks to learn from their mistakes.
1980: Yann LeCun develops the convolutional neural network (CNN), a type of neural
network that is well-suited for image recognition.
1990: Jürgen Schmidhuber and Sepp Hochreiter develop the long short-term memory
(LSTM) network, a type of recurrent neural network that is well-suited for sequential data
such as text and audio.
2006: Geoffrey Hinton and his team show that deep neural networks can be used to
achieve state-of-the-art results on a variety of image classification tasks.
2012: AlexNet, a deep CNN, wins the ImageNet Large Scale Visual Recognition
Challenge (ILSVRC), a prestigious competition for image classification.
2014: Google Translate begins using deep learning models to achieve state-of-the-art
9
results in machine translation.
2016: AlphaGo, a deep learning-powered Go program, defeats Lee Sedol, a professional
Go player.
2017: Self-driving cars powered by deep learning begin to be tested on public roads.
Today, deep learning is one of the most important and rapidly growing fields in
artificial intelligence. Deep learning models are being used to solve a wide range of
problems in many different industries.
3.1 Convolutional Neural Network (CNN)
The convolutional neural network, often known as a CNN, is an example of a
feed-forward artificial neural network. It belongs to the field of deep learning, which is a
subset of machine learning, and can be seen as a rebranded version of Artificial Neural
Figure 3.1: Convolutional neural networks architecture
Networks (ANNs). CNNs have experienced significant advancement in the realm of
image recognition. Consequently, they are particularly utilized for the purpose of
analysing visual data and accomplishing tasks that go beyond simple classification. The
key strength of CNN lies in its capability to derive meaningful features from an image
and utilize these features as the foundation for its training process. Processing and feature
extraction are performed differently compared to a traditional neural network using this
system. For accurate identification of an image's contours, it is necessary to consider the
local arrangement of pixels. CNN begins by detecting smaller local patterns on an image
and then stringing those patterns together to form more complex shapes. Convolutional
10
Neural Networks consist of an input layer, an output layer, multiple hidden layers, and an
extensive set of parameters that facilitate the learning of complex objects and patterns.
CNN subsamples the input data using convolution and pooling processes before applying
an activation function.
The first layer of a standard CNN is the input layer, which is then followed by
layers that use alternating combinations of pooling and convolutional operations.
Ultimately, the fully connected layer at the end of the network is followed by the Soft
Max activation function or output layer.
3.1.1 Convolutional Layer
Convolution layer applies filters that conduct convolution operations to scan the
input I according to its dimensions. It can be defined by adjusting the size of the filter F
and the stride S. The output O of this operation is called feature map or activation map.
The filter and stride layers make up the convolution layer, and it is the most important
part of the CNN core. The convolution process is carried out by calculating the dot
Figure 3.2: Basic convolution operation
11
product between the image pixels and the filter. Furthermore, the computed result gets
added to the filter area. The term "stride" refers to the magnitude of the steps taken during
changing. Following this, the filter proceeds to perform comparable operations across the
complete image area by moving horizontally and vertically.
3.1.2 Pooling Layer
The pooling layer is a down sampling operation typically applied after a
convolutional layer. In particular, the most popular types of pooling layer are max
pooling and average pooling. It is also known as down sampling or subsampling
operation, reduces the size of the output data from the convolution layer. This technique
is employed to decrease the count of parameters that can be learned and the dimensions
of the images, thus simplifying the learning process without compromising the
information and features of the input. Additionally, it proves beneficial in preventing over
fitting. When it comes to various pooling functions, two widely employed ones are max
pooling and average pooling. These techniques effectively decrease the number of
Figure 3.3: Max pooling operation
12
connections to the subsequent layer, which is typically a fully connected layer.
The output of a convolution layer is a 4x4 matrix, and when a max-pooling
operation is applied, the multiplication of those matrices begins at the top-left corner of
the image. The max pooling operation returns the maximum value and then shifts to the
right by an amount determined by the allocated stride value, is equal to 2. Since the
channel is unaffected by the max-pooling operation, the output matrix will always be of
the form 2x2.
3.2 Fast Regional Convolutional Neural Networks (Fast R-CNN)
Fast R-CNN, a seminal deep learning model introduced by Ross Girshick in 2015,
revolutionized object detection by significantly improving both speed and accuracy
compared to its predecessor, R-CNN. This model addressed several inefficiencies in the
original R-CNN architecture, making it more practical for real-world applications. The
fundamental innovation of Fast R-CNN lies in its ability to share computation across
multiple region proposals. Instead of processing each region independently, Fast R-CNN
efficiently extracts features from the entire image using a single forward pass through the
convolutional neural network (CNN). This shared computation dramatically reduces
redundancy and accelerates the object detection process. The Fast R-CNN pipeline
consists of several key components. The first step involves generating region proposals
using an external algorithm, such as Selective Search. These proposals serve as potential
bounding boxes for objects within the image. Unlike R-CNN, which processed each
proposal separately, Fast R-CNN introduced the Region of Interest (RoI) pooling layer to
extract features from all proposals simultaneously. The RoI pooling layer is a critical
component that enables efficient feature extraction. It takes the convolutional feature map
from the entire image and extracts features only from the regions specified by the
proposals. This pooling operation ensures that the features extracted from different
regions are aligned and can be fed into subsequent layers for classification and bounding
box regression. Fast R-CNN incorporates a Region Proposal Network (RPN) into the
model architecture. The RPN operates on the same convolutional feature map used for
object detection, generating region proposals based on anchor boxes. This integration
13
streamlines the overall process, allowing end-to-end training of the model and
eliminating the need for a separate proposal generation step.
The model's loss function combines classification loss, which determines the
object class, and bounding box regression loss, which refines the coordinates of the
proposed bounding boxes. This multi-task loss is crucial for training the network to
accurately classify and localize objects within the image. One notable advantage of Fast
R-CNN is its improved speed. By sharing computation across the entire image, the model
eliminates the need for redundant feature extraction for each region proposal, making it
more computationally efficient during both training and inference. This efficiency made
Fast R-CNN a practical choice for real-time object detection applications. Fast R-CNN
has had a profound impact on the field of computer vision, serving as a foundation for
subsequent models and advancements. Its success paved the way for Faster R-CNN,
which integrated the region proposal generation directly into the model, further
improving efficiency and end-to-end training. Despite its achievements, Fast R-CNN has
limitations, such as the dependence on external proposal generation algorithms and the
sequential nature of the training process. Researchers have continued to build upon the
Fast R-CNN framework, addressing these challenges and pushing the boundaries of
object detection performance. The legacy of Fast R-CNN persists in the ongoing quest for
faster, more accurate, and more efficient deep learning models in the realm of computer
vision.
3.3 Regional Convolutional Neural networks
Region-based Convolutional Neural Networks (R-CNNs) represent a pivotal
advancement in computer vision and object detection. Introduced by Ross Girshick et al.
in 2014, R-CNNs address the limitations of traditional CNNs in handling object
localization tasks. The fundamental challenge R-CNNs tackle is the accurate
identification and localization of objects within an image. R-CNNs operate on the idea of
dividing an image into distinct regions, which are then individually analyzed to determine
the presence of objects. The process begins with the generation of region proposals,
typically achieved through selective search, a method for identifying potential object
14
locations. Each proposed region is then passed through a CNN to extract relevant
features. This feature extraction stage allows R-CNNs to understand the unique
characteristics of different objects present in the proposed regions. The extracted features
are then used to classify the objects within each region and refine the bounding box
coordinates. One notable issue with traditional R-CNNs is their computational
inefficiency, as they process each region independently. The subsequent development of
Figure 3.4: Architecture of Regional Convolutional Neural networks
(Sample figure)
Fast R-CNNs addressed this concern by sharing convolutional features across multiple
region proposals, significantly improving speed. Building upon Fast R-CNNs, Faster RCNNs introduced Region Proposal Networks (RPNs), allowing for an end-to-end
trainable system. RPNs generate region proposals directly from the shared convolutional
features, streamlining the overall process. The integration of RPNs contributes to the
"faster" aspect of Faster R-CNNs, making them more efficient for real-time applications.
R-CNN variants have been widely applied in numerous fields, including autonomous
vehicles, surveillance, and medical image analysis.
The introduction of R-CNNs has led to substantial improvements in object
detection accuracy compared to previous methods. One significant advantage of R-CNNs
is their ability to handle object detection in complex scenes with multiple objects and
varying scales. Despite their success, R-CNNs have limitations, including high
15
computational demands during training and inference. Researchers have continued to
explore improvements and optimizations, leading to the emergence of even more efficient
models. The development of models like YOLO (You Only Look Once) and an image,
improving both speed and accuracy. The interpretability of R-CNNs can be further
enhanced by visualizing attention maps, providing insights into the regions of an image
that contribute most to the model's predictions. The trade-off between model complexity
and efficiency is a key consideration in the design of R-CNN architectures, influencing
choices in network depth and parameter count. The development of R-CNNs has spurred
research into domain adaptation, exploring methods to enhance the model's performance
when applied to data from different distributions. The concept of self-supervised learning
is gaining attention in the context of R-CNNs, allowing models to learn useful
representations from unlabelled data, potentially reducing the need for extensive labelled
datasets. The role of uncertainty estimation in R-CNNs is a topic of ongoing research,
addressing the need for models to quantify their confidence in predictions, especially in
safety-critical applications.
3.4 Masked Regional Convolutional Neural Networks
Mask R-CNN, a ground breaking deep learning model, extends the capabilities of
traditional object detection by incorporating instance segmentation. Introduced by
Kaiming He et al. in 2017, Mask R-CNN builds upon the success of Faster R-CNN,
adding a dedicated branch for predicting segmentation masks alongside bounding boxes
and class labels. The primary objective of Mask R-CNN is to precisely delineate the
contours of individual objects within an image. This is crucial for applications where
understanding the exact boundaries of objects is essential, such as in medical imaging or
robotics. The model achieves this by introducing a parallel mask prediction branch that
operates in tandem with the existing object detection pipeline. The architecture of Mask
R-CNN comprises three main components: the backbone network, the Region Proposal
Network (RPN), and the Mask Head. The backbone network is responsible for feature
extraction, capturing hierarchical representations of the input image. The RPN generates
region proposals, and the Mask Head refines these proposals by predicting both the class
labels and the segmentation masks. Training Mask R-CNN involves a multi-task loss
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function that combines object classification loss, bounding box regression loss, and mask
segmentation loss.
The model is trained end-to-end, leveraging annotated datasets with pixel-level
segmentation masks. One notable advantage of Mask R-CNN is its ability to handle
overlapping and occluded objects, providing a more detailed understanding of complex
scenes. This makes it well-suited for applications like autonomous vehicles, where
accurate instance segmentation is critical for safe navigation. The success of Mask RCNN has led to its widespread adoption in various domains, including image and video
analysis, where a granular understanding of object boundaries is essential. Additionally,
the model's versatility extends to interactive image segmentation tasks, enabling users to
interactively refine or modify segmentation masks. Despite its effectiveness, Mask RCNN has computational demands due to its intricate architecture. Researchers continue to
explore ways to enhance the model's efficiency and speed without compromising its
segmentation accuracy. As deep learning in computer vision progresses, Mask R-CNN
remains a pivotal model, inspiring further innovations in instance segmentation and
object understanding.
3.5 Residual Network-50 (ResNet-50)
ResNet-50 is a type of convolutional neural network (CNN) that has
revolutionized the way we approach deep learning. It was first introduced in 2015
by Kaiming He et al. at Microsoft Research Asia. ResNet stands for residual network,
which refers to the residual blocks that make up the architecture of the network. ResNet50 is based on a deep residual learning framework that allows for the training of very
deep networks with hundreds of layers. The ResNet architecture was developed in
response to a surprising observation in deep learning research: adding more layers to a
neural network was not always improving the results.
This was unexpected because adding a layer to a network should allow it to learn
at least what the previous network learned, plus additional information. To address this
issue, the ResNet team, led by Kaiming He, developed a novel architecture that
incorporated skip connections. These connections allowed the preservation of
17
information from earlier layers, which helped the network learn better representations of
the input data. With the ResNet architecture, they were able to train networks with as
many as 152 layers. The results of ResNet were ground breaking, achieving a 3.57% error
rate on the ImageNet dataset and taking first place in several other competitions,
including the ILSVRC and COCO object detection challenges. This demonstrated the
power and potential of the ResNet architecture in deep learning research and applications.
Let’s look at the basic architecture of the ResNet-50 network.
3.5.1 ResNet-50 Architecture
ResNet-50 consists of 50 layers that are divided into 5 blocks, each containing a
Figure 3.5: Architecture of ResNet-50
set of residual blocks. The residual blocks allow for the preservation of information from
earlier layers, which helps the network to learn better representations of the input data.
1) Convolutional Layers
The first layer of the network is a convolutional layer that performs convolution
on the input image. This is followed by a max-pooling layer that downsamples the output
of the convolutional layer. The output of the max-pooling layer is then passed through a
series of residual blocks.
2) Residual Blocks
Each residual block consists of two convolutional layers, each followed by a batch
normalization layer and a rectified linear unit (ReLU) activation function. The output of
the second convolutional layer is then added to the input of the residual block, which is
then passed through another ReLU activation function. The output of the residual block is
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then passed on to the next block.
3) Fully Connected Layer
The final layer of the network is a fully connected layer that takes the output of
the last residual block and maps it to the output classes. The number of neurons in the
fully connected layer is equal to the number of output classes.
4) Concept of Skip Connection
Skip connections, also known as identity connections, are a key feature of
ResNet-50. They allow for the preservation of information from earlier layers, which
helps the network to learn better representations of the input data. Skip connections are
implemented by adding the output of an earlier layer to the output layer.
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4. INTELLIGENT WASTE CLASSIFIER
Intelligent waste classifier is an autonomous system that uses computer vision and
deep learning to identify and categorize recyclable materials. The system consists of two
main parts: fig 4(a) robotic manipulator and a vision-based material detection and
categorization module.
The robotic manipulator is responsible for physically separating the waste into
different bins according to the material type. The vision-based material detection and
categorization module uses cameras to capture images of the waste and then uses deep
learning to identify the material type of each item. This information is then sent to the
robotic manipulator, which picks up the item and places it in the appropriate bin. The
robotic sorting machine offers several advantages over traditional manual sorting
methods. First, it is more accurate and consistent. Second, it can work 24/7 without
Figure 4.1: Intelligent waste classifier robotic arm
breaks. Third, it is less prone to injury. Fourth, it can handle a wider variety of materials.
It has the potential to revolutionize the waste sorting industry. It could help to increase
the efficiency and effectiveness of waste sorting, which could lead to a decrease in the
amount of waste that is sent to landfills. It could also help to create new jobs in the
robotics industry.
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Figure 4.2: Photograph of intelligent waste classifier in working state
4.1 Components
4.1.1 Waste feeder
Waste feeders are most suitable for handling light, fine, abrasive and free-flowing
materials. Especially these waste feeders are used to dump the solid waste into sorting
machine. They are available as horizontal and inclined Belt Feeders for extracting bulk
materials from under a dump hopper or regulating feed to a crusher or screen. Belt
Feeders help to provide volumetric feed and even metering to prevent flooding. The belt
that carries the material may be a fabric reinforced belt or a steel cable reinforced belt.
The belt is propelled by a Drive Pulley and Shaft. The drive shaft is rotated by a Drive
Figure 4.3: Waste Feeder
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Mechanism. A supplementary equipment may include a feed chute, skirt board, a
discharge chute and a High-Pressure Lubrication System. Belt Feeders are a trusted and
proven economical means of transportation for many industries. A dual feeder shed waste
at a controlled rate onto the belt. The material came from a waste processing installation
that transferred uncontrolled urban waste or a newly developed stream located above the
conveyor to feed the system a controlled waste mix for experimentation purposes
4.1.2 Trommel
First waste is carried through garbage feeder and feeded into trommel then
trommel drum rotates with waste, which at bottom tiny holes are present from that tiny
particles like dust, sand, mud is separated. It is a pre-processing method used to remove
Figure 4.4: Trommel in industry
tiny particles from waste. Then the solid waste is flowed on to conveyor belt.
4.1.3 Conveyor belt:
A conveyor belt is a looped belt that is driven by and wrapped around one or more
pulleys. It is powered by an electric motor and supported by a metal plate bed or rollers
upon which the conveyor belt rests. The pulley that powers a conveyor belt is referred to
as the drive pulley and has an unpowered idler pulley. Pulley drives at the discharge end
of a conveyor belt are referred to as head drives, while ones located at the in feed end are
known as tail drives. The preferred type of pulley drive is a head drive located at the
discharge end and uses pull force to move a conveyor belt. Typically, industrial waste
processing units transfer recyclables on conveyor belts. It is a replica of such a setup
through the installation of a 22.5-m long, 1-m wide (usable width: 0.8 m) belt with a
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Figure 4.5: A Conveyor belt and components
speed of up to 0.15 m/s and an optical encoder system so that the speed could be
monitored.
4.1.4 Camera
A stereo, full-high-definition ZED camera enabled the automated categorization
of recyclable materials. The camera was placed 148 cm in front of the robot (in the
direction in which the recyclables were moving), at a height of 75 cm above the conveyor
belt, looking downward. To ensure constant light conditions during data acquisition and
Figure 4.6: ZED AI based camera
system operation, the camera was placed inside a box with LED equipment. The camera’s
field of view covered the entire width of the conveyor belt, providing information about
the shape and colour of the transported waste.
4.1.5 Robotic arm
An ABB IRB360 delta robot was installed above the conveyor belt to enable the
picking and transfer of waste to bins. The robot consisted of three high-torque
servomotors mounted on a rigid frame, each linked to a different arm. The three arms
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Figure 4.7: ABB IRB360 delta robot (robotic arm) for picking waste
were connected to a central platform, driving it to move very fast and accurately in 3D
space. The robot had a payload of 6 kg, which was appropriate for repetitive and rapidly
completed applications.
4.1.6 Vacuum gripper
Figure 4.8: Vacuum gripper
To enable automated picking and placing, we attached a vacuum gripping module
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to the end effector of the robot; it consisted of a vacuum blower that provided highvolume suction to pick up and hold selected materials. The use of vacuum technology
provides a robust and low-cost solution for material transfer.
The majority of robotic systems for recyclable sorting rely on a pick-and-place
(PnP) process to select objects and physically transfer them to the appropriate material
bin. Capitalizing on delta robots, they developed a composite system that performs fast
Figure 4.9: Two types of air flow. (a) Vacuum suction (blue
arrow). (b) Compressed air (red arrow) used for disposal.
recyclable sorting by adopting the PnP approach. Here venture generators use
compressed air flowing through a conical orifice to develop a pressure difference. Their
main advantages include a short response time that enables fast picking and a powerful
gripping force once the suction cup has sufficiently sealed on an object. The use of
blowers provides an alternative. Blowers spin synchronized rotors in a chamber to
generate a vacuum that is relatively weak but that has a high volume (typically more than
500 m /h). 3
The problem with using blowers for recyclable picking lies in the equipment’s
slow response time, which may significantly decelerate waste processing. Industrial
conditions, it was occasionally impossible for the suction cup to form a seal with objects,
25
resulting in failures to grab items. To compensate for this, the present work incorporates a
blower. The high volume of pumped air increases the system’s ability to pick up
significantly deformed objects when there is no perfect suction cup seal. This comes with
the cost of slowing the processing speed because the blower valve used to activate/
deactivate the vacuum at the end effector has a sluggish response, on the order of 500
ms/cycle. Therefore, we examined methods to gain speed. When picking, the delay was
reduced by sending the vacuum activation command at a properly timed moment while
the robot was moving toward the goal. Moreover, to decrease the material disposal delay,
we developed a solution that forced compressed air toward an item to detach it from the
suction cup. Specifically, a custom T-shaped tube enabled fast switching between the
blower vacuum (negative pressure) and the compressed air (positive pressure), right on
the suction cap (see Figure 4.1.6).
The joint activation/deactivation of binary valves controlling the two air streams
(vacuum versus compressed air) significantly reduced the time needed for item disposal.
When the robot’s end effector reached the bin, the two valves turned off the vacuum and
activated the compressed air, thus making items detach from the suction cup in much less
time.
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5. Deep learning waste identification
For identifying the waste we are using deep learning algorithm. Accordingly, any
autonomous recovery system needs to accurately categorize recyclables based on their
material type. One way to achieve this is to use hyper spectral cameras, although this
significantly increases the cost of composite systems. Alternatively, one may exploit
recent developments in deep learning to come up with an efficient and much cheaper
solution. CNNs trained with deep learning algorithms have been widely applied in
demanding computer vision applications. Interestingly, CNNs have also been used to
perform waste classification, although they were applied to a relatively small data set
with single-item images and without any type of occlusion. In contrast, the present work
targets “instance segmentation” to accomplish multiple object identification and labeling.
To this end, the well-known Mask Regional CNN (R-CNN) open source network was
employed since it has been particularly successful at similar tasks. Mask R-CNN provides
a scalable means for categorizing recyclables into a high number of classes.
5.1 Making Dataset
Figure 5.1: The basic data set, including representations of each class of
studied material: (a) aluminum (first row), paper and cardboard (second row),
PET bottles (third row), and nylon (fourth row). (b) The augmented data set.
Making a dataset which is more precise is a crucial step for accurate results. So
already there were two open source best datasets were available from Trash net and taco
27
but those are not much suitable for industrial use because the garbage which they sort was
filled with dust and many objects are piled one up on another so these datasets are not
suitable. So they made their own dataset, to make it first they took photographs of single
object by allowing to run on conveyor belt with black background as shown in figure 5.1
(a).Then these photos were made as a basic dataset and again made another data set this
time they captured the objects which were used in first dataset with the combination of
other objects and with different background in that some are piled upon one another as
shown in figure 5.1 (b).
Typical image data collection methods assume that photos of objects of interest
will be taken against a variety of backgrounds and manually labeled using annotation
tools. This is a complicated and time-consuming approach that can hardly support the
collection of very large data sets. The steps we took to develop a rich waste data set are
outlined in the following. The resulting mask was assigned as an image annotation to
clearly indicate the region of interest; it was also used for Mask R-CNN training. In the
second step, we randomly applied three geometric transformations (translation, rotation,
and scaling) to the image and the mask. This resulted in a large data set of single-object
images against a black (i.e., a conveyor belt) background. Finally, pairs of objects from
the second step were randomly selected (with their masks) and arbitrarily placed over
new images with colourful backgrounds to develop complex problem instances. In the
case of recyclable object data collection, this step provided a means to examine cases
with multiple, possibly overlapping, objects across a range of diverse backgrounds.
5.1.1 Synthetic complex dataset
They followed the semi-automated procedure summarized earlier to develop the
data set for training Mask R-CNN. They started by collecting 400 images for each of the
material types considered in the study, namely, aluminum, paper and cardboard,
polyethylene terephthalate (PET) bottles, and nylon. Then, we followed a manual
procedure to collect red–green–blue images. All images had the same size, i.e., 800 × 800
pixels, and depicted a single recyclable against a black background. It is noted that, to
have a representative data set of recyclables images, we also considered deformed and
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dirty materials.
Figure 5.2: Four samples from the training data
set presented with annotated masks
These images were processed by following step one (mentioned previously) to
identify masks describing regions of the identified objects. We called this the Basic data
set. Then, we applied step two (from the preceding) to gather 2,000 images of each
material type. We called this the Synthetic Single data set. Finally, following the third
processing step, they developed a data set that artificially described complex cases of
multiple and overlapping objects.
This was the Synthetic Complex data set. In particular, they developed 16,000
images for the training set and 5,600 for the validation set. Figure 5.1.1 includes four
random images from the data set that have been successfully created and annotated
following the data augmentation process. The first column contains original images. The
29
second and the third columns show each material mask and its class identification as a
subtitle.
5.2 Training of Masked R-CNN Model
The development of a vision-based module capable of categorizing recyclables
into different material types was accomplished using Mask R-CNN, which has been
successfully employed to tackle a wide range of object identification and categorization
tasks. Mask R-CNN is a deep CNN that simultaneously predicts recyclable object
bounding boxes, masks, and material types. We used a public Mask R-CNN
implementation that was trained on the data sets in the “Vision-Based Material
Categorization” section, using 30% of the images for model learning validation and the
remaining 70% for model training.
Table 5.1: The training parameters configuration
To increase the image processing frame rate, we incorporated ResNet-50 for
feature extraction across entire photos. Moreover, a region proposal network (RPN) was
employed to scan images in a sliding window manner to identify areas that contained
objects. The RPN employed a set of boxes, called anchors, with predefined image
locations and scales (there were five scales and three aspect ratios in our implementation)
to figure out the size and location of an object on the feature map. Specifying the correct
30
anchor span is critical for obtaining successful classification results. To estimate
recyclable objects’ bounding boxes, masks, and material types, dedicated subnet works
known as heads work on identified regions of interest to shape the final output. After
extensive experimentation with the validation set, we identified the Mask R-CNN
training parameters that worked effectively. Before using Mask R-CNN for recyclable
identification and classification, a learning process was applied to improve the
applicability of the deep neural network to the problem.
To bootstrap the learning process, a network pertained on the Common Objects in
Context data set was used. This approach, introduced in, speeds up learning and ensures
the minimum quality of the results in a reasonable training time. Following this, only the
Mask R-CNN heads are typically trained for a problem, keeping the general object
feature extraction mechanism unchanged. To adapt Mask R-CNN to the classification
task, we developed and tested four customized implementations: Net 1, Net 2, Net 3, and
Net 4. They were trained on a high-performance computing infrastructure with Tensor
Flow GPU support.
5.2.1 Training Analysis
Table 5.2: The training and validation loss
Mask R-CNN training is based on a complex loss function that is calculated as the
31
weighted sum of different Partial losses at every training state. The partial losses
considered in the current implementation are described in the following.
1) mrcnn_bbox_loss: corresponds to the success at localizing the bounding boxes of
objects that belong to a given class. This loss metric is increased when the object
categorization is correct but the bounding box localization is not precise.
2) mrcnn_class_loss: describes the loss due to the improper categorization of an object
identified by the RPN. This provides an indication of how well Mask R-CNN recognizes
each class of an identified object. This metric is increased when an object is detected in
an image but mis-classified.
3) mrcnn_mask_loss: It is the metric that summarizes the success at implementing
masks across a processed image that correspond to the pixel area covered by the
identified objects. The training data set includes masks for all objects of interest in input
images, thus providing the ground truth for the evaluation of masks predicted by Mask RCNN.
4) rpn_bbox_loss: corresponds to the localization accuracy of the RPN, in other words,
how well the RPN identifies individual objects. High values indicate miscalculations
when an object is detected but the bounding box needs to be corrected.
5) rpn_class_loss: It is an RPN performance metric that is increased when an object is
undetected at the final output and decreased when the RPN successfully identifies an
item. Besides tuning the training procedure parameters, there are conditions that may
affect the quality of the obtained results. These include the data set size, the number of
the learning steps per epoch, and the use of additional data augmentation strategies to
improve generalization. Accordingly, several Mask R-CNN training variations were
examined for their training and validation results.
5.2.2 Training results of R-CNN
1) Net 1: We began with the relatively small Basic data set, which consisted of singleobject, black-background images (see the “Vision-Based Material Categorization”
section). The training process ran with 50 training steps per epoch, for a total of 200
epochs (this setup was also followed in the next two cases). Although the network was
successfully trained (Table 5.2.1), an assessment against real industrial images revealed
32
many limitations that rendered this solution useless in practice. This is clearly indicated
in Table 5.2.2, which highlights the recall, precision, and F1 performance of the network
for each material and in total. The limited success of this version provided the driving
force for developing artificial data augmentation algorithms.
2) Net 2: The next attempt considered the much larger Synthetic Single data set for
training Mask R-CNN. The solution yielded slightly better results for identifying
individual items, as demonstrated in Table 5.2.2. However, the average F1 score
remained low, proving poor overall performance. This was mainly because the training
data set presented recyclables against a black (conveyor belt) background. Thus, when
this implicit hypothesis was violated (i.e., due to object occlusion), the network
performance dropped dramatically.
3) Net 3: A major improvement to the data set concerned the synthetic placement of
objects against colorful backgrounds that could also contain other objects. The Synthetic
Complex data set was used to train a new Mask R-CNN (the loss metrics are summarized
Table 5.3: The classification results of R-CNN
in the third row of Table 5.2.2), which provided the first vision-based recyclable
identification and categorization solution that could be potentially applied in the real
world. As shown in Table 5.2.2, the network’s success rate in demanding industrial
conditions significantly increased over that of Net 1 and Net 2.
4) Net 4: To further enhance the Mask R-CNN performance, we made changes in two
directions that improved generalization and robustness. First, random affine
33
transformations.
1. R-CNN model had shown average precision of 89.1%
2. It had shown average recall of 91.8%
3. It had shown average F1 score of 89.4%
4. Approximately masked R-CNN was trained with an accuracy of 90%
(Which you can see in figure 5.2.3)
5.2.3 Obstacles Associated With Detecting And Classifying Solid Waste
1) Variability in waste types: Waste materials are diverse and can vary greatly in their
physical and chemical properties. This variability makes it challenging to develop
accurate waste detection and classification models that can identify different types of
waste materials.
2) Complexity of waste objects: Waste objects can be of different shapes and sizes, and
they can be irregularly shaped, making it difficult to accurately detect and classify them.
Environmental factors: Environmental factors such as lighting conditions, dust, and noise
can impact the accuracy of waste detection and classification systems. For example, low
Figure 5.3: Material classification using Mask R-CNN.
34
lighting conditions can make it difficult to identify waste objects accurately.
3) Cost of equipment: Developing and deploying waste detection and classification
systems can be costly. It requires specialized equipment such as sensors, cameras, and
other technologies, which can be expensive.
4) Training data availability: To develop accurate waste detection and classification
models, large datasets of labelled waste materials are needed. However, these datasets
may not be readily available, making it challenging to develop robust and accurate
models.
5) Human errors: Human errors can also impact the accuracy of waste detection and
classification systems. For example, errors in labelling or sorting waste materials can lead
to incorrect identification and classification of waste.
6) Continuous improvement: As new waste materials are introduced and as waste
materials change over time, waste detection and classification models need to be
continuously improved to ensure their accuracy and effectiveness. This requires ongoing
research and development, which can be time-consuming and costly. Therefore, waste
detection and classification pose several challenges that require innovative solutions to
ensure accurate and effective waste management.
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6. WORKING OF INTELLIGENT WASTE CLASSIFIER
Step 1: First solid waste was picked up with cranes and dumped into garbage feeder.
Step 2: then waste in the feeder is lifted up and dropped into trommel.
Step 3: then rotating trommel filters tiny particles and drop the solid waste on to conveyor
belt.
Step 4: the moving conveyor belt move with constant velocity with solid waste towards
robotic arm.
Step 5: robotic arm sorts the object which was internally connected to system which
identifies waste with the trained Mask R-CNN deep learning algorithm. It monitors in
real time with ZED quad camera.
Step 6: The object is picked with robotic arm which uses vacuum grip to hold the object
firmly.
Step 7: then finally the robotic arm drops the picked waste into right bin by turning off
vacuum.
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7. ADVANTAGES & LIMITATTIONS
7.1 Advantages
1) Robots can sort waste more accurately than humans, which can lead to a reduction in
contamination and an increase in the quality of recycled materials.
2) Robots can sort waste much faster than humans, which can lead to significant time
savings and increased productivity.
3) Robots can automate the waste sorting process, which can lead to significant labor cost
savings.
4) Robots can work in dangerous or difficult conditions, such as in hazardous waste
facilities, which can help to protect workers from injury.
5) Reduced environmental impact: Intelligent waste sorting technology can help to reduce
the environmental impact of waste management by improving the efficiency of the
sorting process and reducing the amount of waste that is sent to landfills.
6) Increased recycling rates: Intelligent waste sorting technology can help to increase
recycling rates by making it easier and more efficient to sort recyclable materials.
7) Improved public health: Intelligent waste sorting technology can help to improve
public health by reducing the exposure of workers and the public to harmful waste
materials.
8) Reduced greenhouse gas emissions: Intelligent waste sorting technology can help to
reduce greenhouse gas emissions by reducing the amount of waste that is sent to
landfills, which produce methane, a potent greenhouse gas.
9) Increased energy production: Intelligent waste sorting technology can help to increase
energy production by converting waste materials into biofuels or other forms of
renewable energy.
7.2 Limitations
1) It cannot sort with 100% accuracy still research need to be done.
2) Climatic conditions may effect the sorting process. Especially in rainy season the
waste will be so, this may reduce the efficiency of machine.
3) It cannot open bottle caps to remove left over water from bottles.
37
4) Robotic waste sorting systems are typically designed to sort a specific type of
waste, such as plastic, metal, or paper. This means that they may not be able to sort all
types of waste, which can require additional sorting steps.
5) The production and operation of robotic waste sorting systems can have an
environmental impact. For example, the robots may require energy to operate, and they
may produce waste materials during their manufacturing and disposal.
6) The adoption of robotic waste sorting technology may lead to job losses in the waste
management industry. It is important to consider the social impact of this technology
when making decisions about its adoption.
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8. CONCLUSION
Current solid waste management practices are no longer sustainable. With the
exploitation of state-of-the art computer vision and robotics technology, the goal of
finding innovative ways to attain sustainable waste management becomes more
achievable and realistic. This work presented an intelligent autonomous system to support
the recovery of recyclable materials.
The system was gradually integrated into the operation of an MRF to support the
treatment of waste processed on a daily basis. In comparison to previous works, this
article accomplished the following:
1) It introduced a blower-based vacuum system to improve the ability of robotic systems
to manipulate recyclables.
2) It provided a new data set of recyclable images and a group of processing tools to
facilitate deep learning research into recyclable categorization.
3) It developed a composite autonomous system that successfully identified, localized,
and categorized recyclables in a demanding industrial environment. The technology
proposed here could significantly impact future waste treatment plants, which are
envisioned to be highly automated facilities where no humans will be in direct contact
with waste and where almost all recyclables will be recovered.
39
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