Detection and Identification of Fish Skin Health Status Referring to Four Common Diseases Based on Improved YOLOv4 Model

Abstract

A primary problem affecting the sustainable development of aquaculture is fish skin diseases. In order to prevent the outbreak of fish diseases and to provide prompt treatment to avoid mass mortality of fish, it is essential to detect and identify skin diseases immediately. Based on the YOLOv4 model, coupled with lightweight depthwise separable convolution and optimized feature extraction network and activation function, the detection and identification model of fish skin disease is constructed in this study. The developed model is tested for the diseases hemorrhagic septicemia, saprolegniasis, benedeniasis, and scuticociliatosis, and applied to monitor the health condition of fish skin in deep-sea cage culture. Results show that the MobileNet3-GELU-YOLOv4 model proposed in this study has an improved learning ability, and the number of model parameters is reduced. Compared to the original YOLOv4 model, its mAP and detection speed increased by 12.39% and 19.31 FPS, respectively. The advantages of the model are its intra-species classification capability, lightweight deployment, detection accuracy, and speed, making the model more applicable to the real-time monitoring of fish skin health in a deep-sea aquaculture environment.