Marine Debris

Marine debris research focuses on developing efficient and scalable methods for detecting and quantifying plastic pollution in both surface and submerged aquatic environments. Current efforts leverage deep learning, employing architectures like YOLO and UNet variants, often incorporating attention mechanisms to improve object detection and segmentation accuracy in challenging underwater or satellite imagery. A major hurdle is the lack of comprehensive, standardized datasets for training and evaluating these models, hindering the development of robust, universally applicable solutions. Improved detection capabilities are crucial for informing effective cleanup strategies and monitoring the extent of marine pollution.