Fish Resource Assessment in the Huoyanshan Waters of Poyang Lake Using DIDSON and Deep Learning Models

To scientifically assess the fish resource status and spatial distribution in the Huoyanshan waters of Poyang Lake for the conservation of endangered species like Coilia nasus, an acoustic survey was conducted using a dual-frequency identification sonar (DIDSON) in July 2024. Fish targets were identified and extracted by combining an Echoview-based identification and deep learning models. Catch statistics were integrated to estimate fish density, abundance, biomass, and spatial distribution patterns. A total of 1891 fish targets were detected. The Echoview model achieved an average accuracy of 90.83%, while the YOLO model attained average precision and recall of 0.941 and 0.869, and the DeepSORT model attained precision and recall of 0.887 and 0.911. The total fish abundance was estimated at approximately 223,775 individuals, with a total biomass of about 199,742 kg. Spatially, fish were predominantly distributed in nearshore areas horizontally and concentrated at depths of 5–15 m vertically. The integrated approach combining DIDSON, Echoview and deep learning models proved effective for high-accuracy fish target identification and resource estimation, with deep learning models offering greater objectivity and processing efficiency. This study provides a technical reference for intelligent fish target identification in sonar images and provides baseline data and a technical reference for subsequent fish resource monitoring and management in the Huoyanshan waters of Poyang Lake.