IWHR_AI_Lable_Floater_V1: 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.

海洋垃圾对海洋生态系统构成严重威胁，及时清除内陆水域的漂浮废弃物可有效阻止漂浮碎屑流入海洋。精准的目标检测系统是高效清理漂浮物的前提条件。然而，水域内复杂的光照条件、小尺寸目标等诸多因素，给漂浮目标检测任务带来了极大挑战。为助力漂浮物污染问题的解决并推动人工智能（AI）技术在水利行业的应用，我们构建了首个基于岸基拍摄设备采集真实水域场景的漂浮物数据集——IWHR_AI_Lable_Floater_V1。该数据集包含3000张带有精准标注信息的图像，可用于支撑基于视觉的水面漂浮物检测任务。我们开展了多组基准实验，以评估主流目标检测算法在该数据集上的性能表现。实验结果显示，包括当前最优模型YOLOv9在内的各类模型，其检测精度均较低，这也印证了漂浮目标检测是一项极具挑战性的任务。