Real-time water surface target detection based on improved YOLOv7 for Chengdu Sand River

It has been a challenge to obtain accurate detection results in a timely manner when faced with complex and changing surface target detection. Detecting targets on water surfaces in real-time can be challenging due to their rapid movement, small size, and fragmented appearance. In addition, traditional detection methods are often labor-intensive and time-consuming, especially when dealing with large water bodies such as rivers and lakes. This paper presents an improved water surface target detection algorithm that is based on the YOLOv7 (you only look once) model to enhance the performance of water surface target detection. We have enhanced the accuracy and speed of detecting surface targets by making improvements to three key structures: the network aggregation structure, the pyramid pooling structure, and the down-sampling structure. Furthermore, we implemented the model on mobile devices and designed a detection software. The software enables real-time detection through images and videos. The experimental results demonstrate that the improved model outperforms the original YOLOv7 model. It exhibits a 6.4% boost in accuracy, a 4.2% improvement in recall, a 4.1% increase in mAP, a 14.3% reduction in parameter counts, and archives the FPS of 87. The software has the ability to accurately recognize 11 typical targets on the water surface and demonstrates excellent water surface target detection capability.