Deep Learning Innovations for Underwater Waste Detection

Underwater pollution has become a critical environmental challenge, threatening marine ecosystems and biodiversity due to the accumulation of plastic, metal, and other debris. This project presents an advanced Deep Learning–based Underwater Waste Detection System designed to accurately identify and classify various categories of underwater waste. Leveraging the cutting-edge YOLOv11 object detection framework, the system performs real-time detection across multiple waste classes, including masks, cans, bottles, nets, tires, gloves, electronics, plastics, and miscellaneous debris. The model is trained on a diverse underwater dataset and evaluated using key performance metrics such as precision, recall, mAP50, and mAP50-95. Results show strong detection capabilities in several classes such as cellphone, pbag, glove, mask, and net, while highlighting challenges in detecting smaller or visually ambiguous objects like metal, plastic, and rod. The system demonstrates the potential of deep learning to enhance marine waste monitoring by providing fast and accurate detection in complex underwater environments. By utilizing YOLOv11’s improved architecture and robust feature extraction, this work contributes to scalable, automated solutions for ocean conservation and supports future innovations in environmental surveillance technologies.

Keywords: Underwater Waste Detection, YOLOv11, Deep Learning, Marine Pollution, Object Detection, Environmental Monitoring, Real-Time Detection, Plastic Waste, Ocean Conservation, Computer Vision.