LAUSR

Underwater target detection using optical images is a challenging yet promising area that has witnessed significant progress. However, fuzzy distortions and irregular light absorption in the underwater environment often lead to image blur and color bias, particularly for small targets. Consequently, existing methods have yet to yield satisfactory results. To address this issue, we propose the Underwater-YCC optimization algorithm based on You Only Look Once (YOLO) v7 to enhance the accuracy of detecting small targets underwater. Our algorithm utilizes the Convolutional Block Attention Module (CBAM) to obtain fine-grained semantic information by selecting an optimal position through multiple experiments. Furthermore, we employ the Conv2Former as the Neck component of the network for underwater blurred images. Finally, we apply the Wise-IoU, which is effective in improving detection accuracy by assigning multiple weights between high- and low-quality images. Our experiments on the URPC2020 dataset demonstrate that the Underwater-YCC algorithm achieves a mean Average Precision (mAP) of up to 87.16% in complex underwater environments.