关键词: 鱼病检测, YOLOv5, 图像处理, 特征提取, 信息服务

Abstract: In order to realize the rapid and accurate identification of surface diseases of fish infected by viruses and bacteria in intensive aquaculture, and help farmers quickly understand the damage degree and distribution of fish diseases in the aquaculture tank, a set of rapid detection system for fish diseases was designed based on the improved YOLOv5 combined with embedded technology. The improved YOLOv5 neural network model was used to generate the candidate boxes of fish diseases and realize the rapid classification of fish diseases. The detection system counted and classified fish diseases according to the data in the candidate box. The classification of fish disease hazards was divided into normal, mild and severe, and a quantitative evaluation system for the degree of fish disease hazards was formed by combining the number of fish with the number of sick fish. Finally, GPRS module was introduced to obtain the location information of detection points and form a heat map of fish diseases at the software end. Model test results showed that the detection accuracy of the improved YOLOv5 model was 99.75%, and the recall rate was 93.21%. Test models mAP50 and mAP50: 95 compared with the original YOLOv5 model, the AP reached 99.38% and 88.09% in the case of a slight decrease in FPS of 3.22 frames, indicating excellent performance, and the memory of the improved model was reduced to 13.6 MB. The improved YOLOv5 model had smaller size, superior performance and strong stability, and was suitable for deployment in the embedded system of fish disease detection. The overall test results of the system show that the system can detect the occurrence of fish disease in real time, and the system can classify the fish disease according to normal, mild and severe, and combine the situation of fish disease with the positioning system to form a visual thermal image.

Key words: fish disease detection, YOLOv5, picture processing, feature extraction, information service