An improved YOLOv8s-based fish target detection algorithm for marine pasture

Marine pasture, a novel technology for ecological balance and sustainable development, heavily relies on cage farming which is typically in deep, complex sea environments, challenging existing fish detection algorithms. Therefore,this paper presents an automatic, cost-effective fish detection system with high precision. By integrating the CBAM attention mechanism, the network's feature perception is enhanced, and the MPDIoU loss function augments small target detection. Trained on fish datasets, the refined YOLOv8s algorithm achieves an mAP of 85.12%, Precision of 80.66%, and Recall of 90.11%, outperforming the standard YOLOv8s with a 0.88% increase in mAP and 1.24% in Recall, deserving being considered for marine fish object detection.