Waste2Wealth: Utilizing Computer Vision
Algorithms to Transform Multi-Class Waste in
Warehouses into Profitable Resources)
Vikram PG Student - DSML PES University Bangalore, India, pg.vikram@gmail.com
Dr Jaya R Faculty Great Learning Bangalore, India Jaya.R@greatlearning.in
Dr Narayana Darapaneni Director - AIML Great Learning Bangalore, India darapaneni@gmail.com
Anwesh Reddy Paduri Research Assistant - AIML Great Learning Bangalore, India anwesh@greatlearning.in
Abstract—Efficient waste management is essential for ensuring
sustainable and environmentally friendly warehouse operations.
This paper introduces an automated waste management system
deployed in a warehouse environment, employing advanced
computer vision techniques, specifically YOLOv5 and ResNet50,CNN and Faster RCNNs.
The system leverages these cutting-edge algorithms for waste
detection and classification, facilitating effective waste segregation. The system’s performance is evaluated in terms of accuracy,
computational time, and hardware complexity, demonstrating
the efficacy of YOLOv5 and ResNet-50 in waste management
applications and their potential for optimizing waste segregation
processes in warehouses.
The current work proposes the use of results from the classification to build an inventory of the classified waste which can act
as a raw material for company specialized in using the Waste to
generate Wealth(income).
I. INTRODUCTION
Waste management plays a pivotal role in warehouse
operations by ensuring proper waste disposal and recycling
while minimizing environmental impact. However,
conventional waste segregation approaches heavily rely
on manual labor, resulting in inefficiencies and potential
safety hazards. To address these challenges, computer vision
techniques have emerged as a promising solution to automate
waste management tasks, enhancing the efficiency and
accuracy of waste segregation.
This paper presents an automated waste management system
implemented in a warehouse environment, utilizing YOLOv5
and ResNet-50, Faster RCNN, CNN. YOLOv5 is a state- ofthe-art object detection algorithm, while ResNet-50 is a
widely adopted image classification model. Faster RCNN
on the other hand combines a region proposal network with
a convolutional neural network to achieve accurate and
efficient object detection in images and a CNN is designed
for processing and analyzing visual data, particularly images,
by utilizing convolutional layers to extract meaningful
features and achieve high-performance tasks such as image
classification and object detection.
These algorithms have been implemented for the purpose
of waste detection and classification, enabling efficient
waste segregation. The main objective of the proposed
system is to enhance waste management processes,
promote environmental sustainability, and create
safer
work environments. Additionally, the system maintains an
inventory of the classified objects, which can be utilized as a
database of available raw materials for external systems, such
as an Inventory Management System.
A. Problem Statement
Design and develop an efficient waste classification and
detection system using Artificial Intelligence and Computer
Vision to act as an inventory database for external sources to
help procure waste as a raw material for their downstream
systems.
B. Proposed Solution
The proposed solution for waste management at a warehouse involves leveraging computer vision technology. By
implementing computer vision algorithms and systems, the
solution aims to improve waste management processes and
efficiency.
The system will utilize cameras or other visual sensors to
capture real-time video or images of the warehouse area.
Computer vision algorithms will then analyze the captured
data to detect and classify different types of waste materials
present in the warehouse.
Once the waste materials are identified and classified, the
system can automatically trigger appropriate actions, such
as alerting the warehouse staff, generating waste disposal
requests, or initiating recycling processes. This helps in optimizing waste segregation and disposal, ensuring that each
type of waste is managed correctly and in an environmentally
friendly manner.
Additionally, the proposed solution can maintain an inventory of the classified waste objects, providing valuable data
for tracking and monitoring waste generation patterns. This
information can be utilized for future waste management
planning, optimizing inventory management, and improving
overall efficiency in the warehouse.
By leveraging computer vision technology, the proposed solution aims to enhance waste management practices, promote
sustainability, reduce operational costs, and establish safer
working environments within the warehouse facility.
Fig. 1. Proposed Methodology
II. RELATED WORK
Christina [1] and colleagues presented a waste management
system with low power consumption that utilizes sensors to
detect and collect the garbage produced daily. The system is
equipped with a GSM module and an Arduino Uno microcontroller to transmit data on three levels of waste.
Kiran [2] and colleagues investigated issues related to waste
management worldwide, identifying improper planning and
inadequate technical support as key factors affecting public
health. The authors proposed four waste management models
based on size, budget, route, and waste processing machines.
The models are accompanied by a risk management module
designed to assist municipal corporations in managing waste
in a manner that is both environmentally and economically
sound.
Navarro [3] and colleagues created a trash bin that
prevents waste contamination during rainfall and transmits
data on its fill level. This approach is particularly useful in
cases where robotic technology is utilized for solid waste
disposal.
Faunch [4] In a related study, Saha and colleagues (2017)
discussed different strategies for waste management, including
methods that generate revenue. The proposed In- ternet of
Things (IoT) technology is used for various purposes, such as
animal feed, recycling, composting, fermentation, landfills,
and incineration.
In their study Jain et al. [5] proposed utilizing wireless sensor
networks and IoT technologies to effectively manage waste,
including the real-time monitoring of containers and their fill
levels.
Rishab [6] et al. investigated waste management through
two analytical methods: panel data order
and bootstrapped truncated regression. The study concluded
that certain political and socio-economic factors of local
governments can improve cost-efficiency.
Wei Liu [7] provided an overview of the LDAT landfill
model, which involves using input and output data to calculate
the degradation of conventional waste characteristics.
Marzouk [8], developed a waste management model that
involves various data normalizations. They confirmed the
importance of economic scale and emphasized the crucial role
of having an appropriate waste facility inminimizing costs
Mekal [9] described a geographic information system that
facilitates the targeted collection of municipal solid waste in
developing cities. The research reveals that incorporating both
formal and informal recycling practices is a significant
benefit
Fran [10] et al. conducted a life cycle assessment to evaluate
the recovery of recyclable materials from municipal solid
waste management systems, emphasizing the need for critical
analysis.
Sreejith [11] used a pioneering approach to derive a global
inefficiency score and individual inefficiency scores for each
variable included in the model. The findings showed that only
one-third of the evaluated municipalities were eco-efficient in
their service provision.
Stephanie et al[11] described the assessment of eco-efficiency
in municipal solid waste services with respect to exogenous
variables. The findings indicate the presence of a stable and
accessible market for solid waste.
White [12] employed machine learning techniques to estimate
the generation rate of various plastic wastes and proposed
revenue recovery through recycling.
Xu [13] et.al explored the potential of artificial neural
networks (ANN) to address problems related to solid waste
management. The study found that ANN is a commonly
utilized tool in the literature for predicting waste generation
and
technological
parameters.
III. METHODS
Data Collection: Gather a dataset of images or videos that
represent different types of waste typically found at the warehouse. This dataset should include various waste materials,
shapes, and sizes to ensure the model’s robustness. This project
uses publicly available TACO dataset
Annotation: Annotate the collected images or videos by manually labeling the waste objects in the data. This annotation
process involves identifying and marking the different waste
types present in each image or frame.
Dataset Preparation: Split the annotated dataset into training
and testing subsets. The training set will be used to train the
computer vision model, while the testing set will be used to
evaluate the model’s performance.
Model Selection: Choose an appropriate computer vision
model for waste classification. Convolutional Neural Networks
(CNN), YOLO (You Only Look Once), or Faster R-CNN are
popular choices for object detection and classification tasks.
Model Training: Train the selected computer vision model
using the annotated training dataset. This involves optimizing
the model’s parameters and weights to accurately classify and
detect waste objects.
Performance Evaluation: Assess the performance of the trained
model using the annotated testing dataset. Measure metrics
such as accuracy, precision, recall, and F1 score to evaluate
the model’s ability to correctly classify waste objects.
Deployment: Integrate the trained model into a system that can
perform real-time waste classification at the warehouse. This
system may include cameras or sensors to capture images or
video footage, which will be processed by the computer vision
model to identify and categorize waste items.
Continuous Improvement: Regularly update and refine the
computer vision model using additional annotated data and
feedback from the deployed system. This iterative process
helps improve the model’s accuracy and adaptability to changing waste scenarios.
IV. RESULTS
V. ANALYZING RESULTS FROM VARIOUS MODELS
Fig. 2. Analysis of Results from CNN
Figure 10 illustrates the image classification process
carried out by a CNN model, along with the corresponding
probability graphs. These graphs display the likelihood of
different classifications for the images. The highest probability
is assigned to the glass class, indicating that the algorithm
has a strong confidence in its classification. In most cases, the
algorithm predicts only one class for the images. However, for
the glass class, the algorithm also predicts two other classes,
metal and plastic. This can be attributed to the similarities
between the current image and the images used to train the
network to recognize metal and plastic materials.
The network was trained using a dataset of 2500 images, with
a train/test split ratio of 90/10. Initially, a batch size of 32 was
used during the training process. However, due to limitations
in the system’s capacity for training the network, the batch
size was reduced to 8. This adjustment helped to ensure that
the training process could be executed successfully within the
available system resource
A. Analyzing Accuracy Results from CNN
Fig. 3. Analysis of Accuracy Results from CNN
The accuracy trend during the training of the network is
depicted in Figure. Initially, the graph exhibits minor fluctuations in accuracy. However, over time, the accuracy steadily
improves and follows an upward trend. Eventually, the network
achieves a final accuracy of 80
B. Analyzing Accuracy Results from CNN
Figure depicts the overall loss of the CNN during the
training process, similar to the accuracy graph shown in Figure
12. The loss graph exhibits fluctuations in each iteration, but
overall, there is a downward trend indicating a reduction in
loss over time.
However, as the training progresses towards the 100th epoch,
a noticeable increase in the gap between the training and
validation loss is observed. This widening difference suggests
that the model is starting to overfit the training data, which
necessitates taking preventive measures in future runs to
mitigate overfitting and improve the model’s generalization
ability.
Fig. 6. RESNET 50 results
Fig. 7. RESNET50 results
VI. ANALYZING RESULTS FROM VARIOUS MODELS
Fig. 4. Analysis of Accuracy Results from CNN
C. Analyzing Results from YOLO V5
Fig. 8. Average results
Fig. 5. YOLO V5 results
The training duration of the YOLO network was monitored
and recorded. Initially, similar to the CNN algorithm, the batch
size had to be decreased from 32 to 8 due to limitations
in the capabilities of the computer system. The network
underwent training for 51 epochs, utilizing a specific learning
rate. Subsequently, an additional 49 epochs of training were
performed, but with a reduced learning rate. This approach was
implemented to mitigate overfitting by decreasing the learning
rate when the loss reached a plateau.
After the completion of training, the algorithm was tested on
a dataset comprising over two hundred images. The evaluation
results indicated an accuracy of 66
Hence, even though the YOLO algorithm required a longer
training time compared to the simple CNN, it achieved higher
accuracy.
D. Analyzing Results from RESNET50
Comparatively the accuracy using RESNET50 upon 10
iterations fared better than all the models
Figure displays the percentage of correctly classified images
for each class, consisting of a total of 252 images.
The classifier achieves an accuracy of 88 percent for the
cardboard and metal classes, correctly identifying them. However, the paper and plastic classes exhibit lower accuracy
due to certain challenges. The similarity between many paper
images and cardboard images, as well as the resemblance
between some plastic images and glass, contributes to the
lower accuracy in classifying these materials.
VII. CONCLUSION
In conclusion, implementing computer vision technology for
waste management at a warehouse offers significant opportunities for generating wealth and maximizing value from waste
materials.
By leveraging computer vision algorithms and systems, the
solution enables efficient identification, sorting, and classification of different types of waste in real-time. This enhanced
waste management process allows for effective segregation of
valuable materials that can be potentially reused, recycled, or
sold.
Through accurate identification and classification of waste
materials, the system can identify high-value items or materials
that can be extracted from the waste stream. This opens up
avenues for revenue generation by recovering and repurposing
valuable resources that would have otherwise been discarded.
Additionally, by maintaining an inventory of classified waste
objects, the system provides valuable data that can be used for
strategic decision-making and optimization. This data can contribute to the development of innovative business models and
partnerships with recycling companies or other organizations
involved in waste valorization, leading to increased revenue
streams.
Furthermore, the implementation of computer vision technology streamlines waste management processes, reducing operational costs and improving overall efficiency. By automating
waste detection and classification, the system minimizes manual effort, enhances productivity, and allows the workforce to
focus on more value-added tasks.
Overall, the integration of computer vision technology in
waste management at a warehouse not only contributes to
environmental sustainability but also presents opportunities
for generating wealth. It enables the identification and extraction of valuable resources, enhances operational efficiency,
and facilitates strategic decision-making for maximizing the
economic potential of waste materials.
ACKNOWLEDGMENT
This is the acknowledgment section.
references
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