Fish Tracking and Counting Method Based on Horizontal Similarity Matching Mechanism

Multitarget accurate fish counting is crucial for the intelligent monitoring of water ecology and the intensive cultivation industry, playing a significant role in the protection of aquatic ecological environments and the modernization of fish farming. Existing methods for accurate tracking and counting fish with multiple targets are primarily suitable for ideal situations such as clear fish appearance, slow swimming speed, and stable direction. However, they often prove ineffective in complex real-life situations such as mutual occlusion, rapid swimming, and changeable direction of fish. Therefore, the lightweight target detection model YOLOv5n is combined and a method for tracking and counting fish based on the matching mechanism of horizontal similarity is proposed. This method regards the fish counting problem as a multitarget detection and tracking problem, proposes a horizontal similarity matching mechanism, and optimizes the Simple Online and Realtime Tracking (SORT) algorithm. The horizontal distance of the center point of the detection frame is limited using the position relationship between individual fish in the high-speed water flow to effectively solve the problem of target matching confusion in the SORT algorithm and significantly improve the tracking performance. The results show that the performance of the proposed method is significantly better than that of existing methods on a multitarget tracking dataset. Additionally, the target tracking performance significantly improves under the conditions of target occlusion and direction change. The proposed method has the advantages of simple structure and easy application.