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Plastic has imbibed itself as an indispensable part of our day to dayactivities, becoming a source of problems due to its non-biodegradable natureand cheaper production prices. With these problems, comes the challenge ofmitigating and responding to the aftereffects of disposal or the lack of properdisposal which leads to waste concentrating in locations and disturbingecosystems for both plants and animals. As plastic debris levels continue torise with the accumulation of waste in garbage patches in landfills and morehazardously in natural water bodies, swift action is necessary to plug or ceasethis flow. While manual sorting operations and detection can offer a solution,they can be augmented using highly advanced computer imagery linked withrobotic appendages for removing wastes. The primary application of focus inthis report are the much-discussed Computer Vision and Open Vision which havegained novelty for their light dependence on internet and ability to relayinformation in remote areas. These applications can be applied to the creationof edge-based mobility devices that can as a counter to the growing problem ofplastic debris in oceans and rivers, demanding little connectivity and stilloffering the same results with reasonably timed maintenance. The principalfindings of this project cover the various methods that were tested anddeployed to detect waste in images, as well as comparing them against differentwaste types. The project has been able to produce workable models that canperform on time detection of sampled images using an augmented CNN approach.Latter portions of the project have also achieved a better interpretation ofthe necessary preprocessing steps required to arrive at the best accuracies,including the best hardware for expanding waste detection studies to largerenvironments.

Implementing Edge Based Object Detection For Microplastic Debris