DDEYOLOv9: Network for Detecting and Counting Abnormal Fish Behaviors in Complex Water Environments

Abstract

Accurately detecting and counting abnormal fish behaviors in aquaculture is essential. Timely detection allows farmers to take swift action to protect fish health and prevent economic losses. This paper proposes an enhanced high-precision detection algorithm based on YOLOv9, named DDEYOLOv9, to facilitate the detection and counting of abnormal fish behavior in industrial aquaculture environments. To address the lack of publicly available datasets on abnormal behavior in fish, we created the “Abnormal Behavior Dataset of Takifugu rubripes”, which includes five categories of fish behaviors. The detection algorithm was further enhanced in several key aspects. Firstly, the DRNELAN4 feature extraction module was introduced to replace the original RepNCSPELAN4 module. This change improves the model’s detection accuracy for high-density and occluded fish in complex water environments while reducing the computational cost. Secondly, the proposed DCNv4-Dyhead detection head enhances the model’s multi-scale feature learning capability, effectively recognizes various abnormal fish behaviors, and improves the computational speed. Lastly, to address the issue of sample imbalance in the abnormal fish behavior dataset, we propose EMA-SlideLoss, which enhances the model’s focus on hard samples, thereby improving the model’s robustness. The experimental results demonstrate that the DDEYOLOv9 model achieves high Precision, Recall, and mean Average Precision (mAP) on the “Abnormal Behavior Dataset of Takifugu rubripes”, with values of 91.7%, 90.4%, and 94.1%, respectively. Compared to the YOLOv9 model, these metrics are improved by 5.4%, 5.5%, and 5.4%, respectively. The model also achieves a running speed of 119 frames per second (FPS), which is 45 FPS faster than YOLOv9. Experimental results show that the DDEYOLOv9 algorithm can accurately and efficiently identify and quantify abnormal fish behaviors in specific complex environments.