| Abstrakt: |
Underwater object detection is a challenging task in the process of marine exploration and development. Addressing the issue of poor underwater target detection performance in existing algorithms due to problems such as low visibility and color distortion in underwater images, an improved YOLOv7 underwater target detection algorithm is proposed with the aim of improving underwater object detection performance. Firstly, a multi-information flow fusion attention mechanism (spatial group-wise coordinated competitive attention, SGCA) is designed to solve the problem of feature loss caused by the loss of global context information of the image in the convolution process. It improves the detection accuracy of the model in the case of image blur. Additionally, the switchable atrous convolution (SAConv) module is used to replace the 3×3 convolution module in the ELAN structure to enhance the feature extraction capability of the backbone network. Secondly, Wise-IoU is used as the loss function in the prediction part, which obtains more accurate detection results by balancing model training outcomes on images of varying quality. Finally, an underwater image enhancement method based on dark channel prior (DCP) and depth transmission maps is employed to enhance the images in the underwater dataset. Experimental results show that the improved algorithm achieves a mAP of 87.3% on the selfbuilt underwater object detection dataset, which is 3.4 percengtage points higher than that of the original YOLOv7 algorithm. On the enhanced underwater image dataset, mAP is 87.1%, increases 2.1 percentage points. Therefore, the proposed approach exhibits superior performance in underwater object detection tasks. [ABSTRACT FROM AUTHOR] |
