Progress Report on the Development of an autonomous F450 quadcopter-based
sentinel capable of automatic landing and recharging (Week 7)
Name: Paul Okewunmi
Date: 7th January 2024
Paul Okewunmi Work Plan
Workplan:
I. Ongoing tasks
● Development and testing of machine learning models to automatically detect
events and objects of interest
II. Report on activities during the week
● I completed and tested the pipeline I came up with for the vehicle colour
classification, In summary, it begins with detecting vehicles and identifying their
bounding boxes, then crops the vehicle out of the frame, extracts its dominant
colour using Kmeans clustering, processes HSV values, and finally predicts the
vehicle's colour class.
Output Video: This video shows the result of the colour classification, It can be
observed that the model struggles with the colour brown, and instead
misclassifies it as blue, this can be attributed to class imbalance, the dataset I
gathered contained fewer samples for the brown class, and this can be solved by
gathering more samples.
III. Self-Appraisal
Last week’s indicators were partially met due to the progress in classification with
vehicle detection, but some work still needs to be done regarding the video
transmission to enable this to translate well to SN2 (sn2 video example)
IV. Proposed next steps
● Improving colour classification accuracy and more data gathering.
● Following our midweek conversation, there’s a need for a dataset of vehicles and
their colours from a top-down drone view. So plans will be put in place to create
one and I’ll be submitting a first draft of the execution of this task on Monday.
V. Indicators.
● A complete work plan for the data gathering task, detailing needed resources and
objectives.