Underwater Trash Detection with YOLO: Enhancing Environmental Monitoring in Aquatic Ecosystems

This study explores the application of YOLOv8, a cutting-edge object detection framework, to detect underwater waste. As concerns about protecting marine ecosystems grow, the effective identification and removal of underwater debris have become critical. The research adapts YOLOv8 to address challenges such as variable lighting, distortions, and occlusions in underwater environments. A specialized dataset, containing various underwater landscapes and debris types, was created to train and evaluate the model. Experimental results show strong performance across different YOLOv8 variants: YOLOv8s achieved 78.4% precision, YOLOv8m 83.4%, and YOLOv8l reached 87.8% precision. These findings highlight YOLOv8l's potential for environmental monitoring and conservation efforts, demonstrating its value for underwater waste detection and supporting future advancements in automated surveillance systems designed to protect underwater ecosystems.