Debris Detection

Debris detection research focuses on developing automated systems to identify and locate various types of debris, from space junk to marine plastic, using advanced image processing techniques. Current efforts leverage deep learning models, such as YOLO and Faster R-CNN variants, often incorporating attention mechanisms to improve accuracy and efficiency, particularly in challenging conditions like low light or low signal-to-noise ratios. These advancements are crucial for improving environmental monitoring, enhancing space situational awareness, and enabling autonomous cleanup operations, ultimately contributing to safer and healthier ecosystems.