An Annotated Dataset and Benchmark for Detecting Floating Debris in Inland Waters

Marine litter is a serious threat to marine ecosystems, and the timely removal of floating waste from inland waters is effective in preventing floating debris from entering the sea. An accurate object detection system is a prerequisite for efficiently clearing floaters. However, complex light conditions in the water, small size objects and other factors pose a huge challenge for floating object detection. In order to facilitate the solution of the floating object pollution problem and promote the application of AI technology in the water industry, we proposed the first floater dataset of waters collected from real water scenarios based on shore-based filming equipment, IWHR_AI_Lable_Floater_V1. The dataset consists of 3000 images containing accurate annotation information to support vision-based water surface floater detection tasks. We conducted a number of baseline experiments to evaluate the performance of mainstream object detection algorithms on this dataset. The results show that the detection accuracies of the models, including the state-of-the-art model YOLOv9, are all low, which also indicates that floating object detection is a challenging task.