HRA-YOLO: An Effective Detection Model for Underwater Fish

Abstract

In intelligent fisheries, accurate fish detection is essential to monitor underwater ecosystems. By utilizing underwater cameras and computer vision technologies to detect fish distribution, timely feedback can be provided to staff, enabling effective fishery management. This paper proposes a lightweight underwater fish detection algorithm based on YOLOv8s, named HRA-YOLO, to meet the demand for a high-precision and lightweight object detection algorithm. Firstly, the lightweight network High-Performance GPU Net (HGNetV2) is used to substitute the backbone network of the YOLOv8s model to lower the computational cost and reduce the size of the model. Second, to enhance the capability of extracting fish feature information and reducing missed detections, we design a residual attention (RA) module, which is formulated by embedding the efficient multiscale attention (EMA) mechanism at the end of the Dilation-Wise Residual (DWR) module. Then, we adopt the RA module to replace the bottleneck of the YOLOv8s model to increase detection precision. Taking universality into account, we establish an underwater fish dataset for our subsequent experiments by collecting data in various waters. Comprehensive experiments are carried out on the self-constructed fish dataset. The results on the self-constructed dataset demonstrate that the precision of the HRA-YOLO model improved to 93.1%, surpassing the original YOLOv8s model, while the computational complexity was reduced by 19% (5.4 GFLOPs), and the model size was decreased by 25.3% (5.7 MB). And compared to other state-of-the-art detection models, the overall performance of our model shows its superiority. We also perform experiments on other datasets to verify the adaptability of our model. The experimental results on the Fish Market dataset indicate that our model has better overall performance than the original model and has good generality.