Underwater object detection is a challenging task due to issues caused by the complex underwater environment, including high similarity between backgrounds and objects, large variance in object scales, and serious blurring of objects. These issues lead to poor performance of conventional object detection methods in underwater scenes. To address these difficulties, we propose a high-resolution underwater object detection network based on multi-scale attentional feature fusion. First, HRNet is utilized to extract high-resolution features while preserving target details. Second, a multi-scale attentional fusion module is designed to effectively integrate multi-scale information in channel and spatial dimensions. Finally, DIoU loss is adopted to improve prediction box regression accuracy, and data augmentation is used to enhance network robustness. Experimental results demonstrate that compared to other object detection methods, our approach achieves better detection performance.
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