Rapid detection of fish with SVC symptoms based on machine vision combined with a NAM-YOLO v7 hybrid model

Surveillance of fish disease is essential to contain the spreading epidemics. Current surveillance mostly relies on molecular biology testing methods, which often requires complex procedures and trained operators. These techniques may hardly to be used in many small aquaculture farms. In this paper, a method based on machine vision combined with a target detection network is proposed. A convolutional neural network (CNN) was developed to detect fish infected with SVCV by analyzing the images. The datasets for the CNN that is implemented from a YOLO v7 deep learning algorithm are information extracted from images containing fish populations. An Auto-MSRCR algorithm was used to adaptively enhance the images to minimize manual intervention. We introduced a novel NAM Attention mechanism integrated with the ELAN module in the original YOLO v7 deep learning algorithm. Both channel attention and spatial attention modules were utilized to suppress the insignificant features in the datasets to achieve a more precise and efficient detection method. Also, a loss function MPDIoU was incorporated to avoid missing detection by this vision-based methodology. After training of the model, the detection testing results show that the NAM-YOLO v7 network achieves over 95% prediction accuracy and 93.8% recall, which is superior than other state-of-art YOLO series models. Also, the time for detection for each image only takes 0.18 s illustrating the integrated modules improved the computation efficiency. This novel technique is of great potential to be applied in small farms for rapid and early detection. It can be a vital supplementary tool in developing more effective surveillance strategy by combined with other established methods.