MACHINE LEARNING FOR ENVIRONMENTAL SUSTAINABILITY: DETECTING UNDERWATER WASTE

Abstract

This initiative tackles critical environmental challenges affecting aquatic ecosystems by targeting
underwater debris detection and comprehensive water quality assessment. It leverages advanced machine
learning techniques, with a focus on the YOLO (You Only Look Once) object detection algorithm, celebrated for
its precision and efficiency in identifying and classifying underwater waste. The approach includes a robust suite
of tools, such as structured training notebooks for seamless model training, inference scripts for real-time
application of trained models, and detailed application code to support further advancements. These resources
aim to empower practitioners and researchers in addressing water pollution while promoting safer water sources
and cleaner oceans. By enabling accurate detection and classification of underwater debris and fostering water
quality monitoring, the initiative supports healthier ecosystems and enhances community well-being. Cleaner
waterways play a pivotal role in safeguarding environmental integrity and public health, ensuring a sustainable
future. Through its comprehensive methodology and accessible resources, this initiative aspires to contribute
significantly to environmental preservation and the restoration of aquatic ecosystems.