Detection Method of Aggregated Floating Objects on Water Surface Based on Attention Mechanism and YOLOv3

With the development of smart water conservancy construction, a realistic demand for using computer vision technology to assist in the supervision of floating objects on the water surface has arisen. To address the problem that the current research on water surface floating objects detection mainly focuses on detecting scattered individual floating objects, and an improved YOLOv3 algorithm embedded with the SE attention module is proposed for detecting aggregated water surface floating objects. A self-made dataset containing aggregated floating objects “mixed garbage” and “water pollution” is developed and augmented with four data enhancement methods. The K-means++ algorithm was used to replace the K-means algorithm for clustering the dataset with ground truth box sizes to reduce the negative effects of randomly selecting the initial clustering centers. The localization loss in the loss function of the YOLOv3 model is improved, and GIoU Loss is introduced to improve the localization accuracy. The experimental results show that S-YOLOv3 outperforms other models in the field of water surface object detection on the self-made dataset compared with YOLOv3 and other commonly used models in the field of water surface object detection, and the mAP reaches 83.1%.