Implementing Edge Based Object Detection For Microplastic Debris

Abstract

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.