A novel automatic detection method for abnormal behavior of single fish using image fusion

Fish behavior detection is extremely important for farmers to get information on life indicators of fish, which could be useful to prevent disease outbreaks, predict water quality changes, and improve fish welfare. However, conventional fish disease detection does not meet real-time detection requirements and could have an irreversible influence on fish. Additionally, abnormal detection based on school behavior does not allow for the detection of early abnormal behavior in single fish. To solve this problem, a novel method of abnormal behavior detection based on image fusion was proposed. Firstly, the outline information of the moving object was extracted based on image processing technology. Secondly, the position information of the fish image was enhanced using mosaic image fusion. Finally, bidirectional feature pyramid network, coordinate attention block, and spatial pyramid pooling were added to YOLOv5, which was named BCS-YOLOv5. And compared with the other two typical models, the BCS-YOLOv5 based image fusion achieved the best accuracy with an average accuracy of 96.69% at 45 frames per second in four typical behavior datasets. The proposed method not only improves the extraction of location information but also quantitatively detects similar anomalous behavior, which meets the demand for real-time detection of fish abnormal behavior in aquaculture.