Lightweight underwater object detection method based on multi-scale edge information selection.

Underwater object detection is of great significance to marine ecosystems and underwater biodiversity. However, uneven lighting, color distortion, and noise interference in underwater environments severely impact image quality, significantly reducing detection robustness. With limited computational power and storage space, underwater equipment often cannot meet the demands for efficient processing. As a result, the YOLO algorithm has been widely applied in underwater object detection. This paper proposes a lightweight underwater detector, MAW-YOLOv11, based on multi-scale edge information selection. First, dark channel prior is used to estimate the fog concentration in the image, restoring image clarity and enhancing the recognizability of the target. Next, an innovative Multi-Scale Edge Information Select (MSEIS) module is proposed, and based on MSEIS, the C3kMSEIS module is subsequently introduced. These modules are individually incorporated into the C3K2 module of the backbone network, with the aim of extracting features at multiple scales, emphasizing edge information, and efficiently selecting key features that are highly relevant to the target task, thereby improving the model's accuracy in recognizing critical targets. Third, the ADown downsampling structure is employed to significantly reduce computational overhead while maintaining the original precision. Finally, by using the boundary box regression loss WIoUv3 based on the dynamic focusing mechanism as the loss function, the model's attention to low-quality samples is increased, effectively reducing the negative impact of these samples on the model's performance. Experimental results show that on the URPC dataset, the mAP of MAW-YOLOv11 reaches 81.4%, an improvement of 2.1% over YOLOv11, with a parameter count of 2.11M, a reduction of 0.47M compared to YOLOv11. Comparative experiments with other mainstream object detection algorithms validate the effectiveness and superiority of the proposed method.