Research on nighttime intelligent monitoring method for deep-sea cage fish school based on water surface infrared images

Obtaining fish school information on its size and behavior through fish school monitoring is an important way to improve the efficiency of deep sea aquaculture and reduce costs. In this study, an intelligent fish school monitoring method is proposed by using infrared cameras mounted on a net cage for data collection, in addition to the latest deep learning techniques for model training. The method involves three functional modules: fish detection, fish segmentation and fish pose determination. Firstly, fish images were collected by infrared cameras and manually annotated to build datasets, while an improved faster RCNN model that uses Mobilenetv2 and FPN network as feature extractors to improve detection accuracy is adopted to output bounding boxes of individual fish. Secondly, the top 20% of brightness pixels in the block map were selected as segmentation prompt points, and the image was segmented using Segment Anything Model to generate fish segmentation results. Finally, the fish pose information was determined by applying elliptical fitting using fish segmentation results. After 100 epochs of training, the average precision (AP) of the improved Faster RCNN model reached 84.5%, and the detection time per image was 0.042 s. The results indicate that the proposed method can achieve automatic monitoring of fish school on infrared images and extract effective information.