Implementing Edge Based Object Detection For Microplastic Debris
Amardeep Singh Prof. Charles Jia Prof. Donald Kirk
Plastic has imbibed itself as an indispensable part of our day to day
activities, becoming a source of problems due to its non-biodegradable nature
and cheaper production prices. With these problems, comes the challenge of
mitigating and responding to the aftereffects of disposal or the lack of proper
disposal which leads to waste concentrating in locations and disturbing
ecosystems for both plants and animals. As plastic debris levels continue to
rise with the accumulation of waste in garbage patches in landfills and more
hazardously in natural water bodies, swift action is necessary to plug or cease
this flow. While manual sorting operations and detection can offer a solution,
they can be augmented using highly advanced computer imagery linked with
robotic appendages for removing wastes. The primary application of focus in
this report are the much-discussed Computer Vision and Open Vision which have
gained novelty for their light dependence on internet and ability to relay
information in remote areas. These applications can be applied to the creation
of edge-based mobility devices that can as a counter to the growing problem of
plastic debris in oceans and rivers, demanding little connectivity and still
offering the same results with reasonably timed maintenance. The principal
findings of this project cover the various methods that were tested and
deployed to detect waste in images, as well as comparing them against different
waste types. The project has been able to produce workable models that can
perform on time detection of sampled images using an augmented CNN approach.
Latter portions of the project have also achieved a better interpretation of
the necessary preprocessing steps required to arrive at the best accuracies,
including the best hardware for expanding waste detection studies to larger
environments.