Underwater Target Detection Based on Improved YOLOv7 Algorithm With BiFusion Neck Structure and MPDIoU Loss Function

Underwater target detection has developed greatly in recent years. However, the accuracy of underwater target detection is limited by the complex underwater environment. Based on YOLOv7, we propose an underwater object detection algorithm model to improve precision and confidence (BiFusion Neck module and a MPDIoU loss function). Compared to traditional networks module, the Bifusion Neck module preserves more features from the lower layers by utilizing the output of the P2 feature layer. Moreover, the loss function was improved on the basis of IoU introducing Minimum Point Distance. Finally, the LSKA attention mechanism is introduced to enhance the feature extraction of targets at different scales. The experimental results demonstrate that the BFD-YOLO model proposed in this study achieves an average detection accuracy(mAP50) of 84.8% on a customized dataset, surpassing the performance of the YOLOv7 algorithm by 11.5% and outperforming other tested algorithms. Furthermore, the BFD-YOLO algorithm exhibits strong performance on various datasets and demonstrates superior generalization capabilities.