A Dynamic Interference Detection Method of Underwater Scenes Based on Deep Learning and Attention Mechanism

Improving the three-dimensional reconstruction of underwater scenes is a challenging and hot topic in the field of underwater robot vision system research. High dynamic interference underwater has always been one of the key issues affecting the 3D reconstruction of underwater scenes. However, due to the complex underwater environment and insufficient light, existing target detection algorithms cannot meet the requirements. This paper uses the YOLOv8 network as the basis of the algorithm and proposes an underwater dynamic target detection algorithm based on improved YOLOv8. This algorithm first improves the feature extraction layer of the YOLOv8 network, improves the convolutional network structure of Bottleneck, reduces the amount of calculation and improves detection accuracy. Secondly, it adds an improved SE attention mechanism to make the network have a better feature extraction effect; in addition, the confidence box loss function of the network is improved, and the CIoU loss function is replaced by the MPDIoU loss function, which effectively improves the model convergence speed. Experimental results show that the mAP value of the improved YOLOv8 underwater dynamic target detection algorithm proposed in this article can reach 95.1%, and it can detect underwater dynamic targets more accurately, especially small dynamic targets in complex underwater scenes.