Qiao Guangchao, Yang Mingxiang, Wang Hao
China Institute of Water Resources and Hydropower Research, Beijing, China.
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing, China.
Sci Data. 2025 Mar 5;12(1):385. doi: 10.1038/s41597-025-04594-9.
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垃圾对于防止漂浮碎片进入海洋十分有效。精确的目标检测系统是高效清除漂浮物的前提条件。然而,水中复杂的光照条件、小尺寸物体等因素给漂浮物检测带来了巨大挑战。为便于解决漂浮物污染问题并推动人工智能技术在水行业的应用,我们基于岸基拍摄设备提出了首个从真实水域场景采集的水域漂浮物数据集——IWHR_AI_Lable_Floater_V1。该数据集由3000张包含精确标注信息的图像组成,以支持基于视觉的水面漂浮物检测任务。我们进行了多项基线实验,以评估主流目标检测算法在该数据集上的性能。结果表明,包括最先进的模型YOLOv9在内的模型检测准确率都很低,这也表明漂浮物检测是一项具有挑战性的任务。