YOLO-VIS: Human Vision Mechanism Enhanced YOLO for Forward-Looking Sonar Images Object Detection

Forward-looking sonar images object detection plays a crucial role in marine resource exploration and national defense. Existing methods typically focus on traditional feature extraction approaches when processing sonar images, but these methods have not fully borrowed from the advanced processing mechanisms of the human brain in target recognition, leading to less than satisfactory performance in forward-looking sonar images with issues such as low resolution, dynamic changes, and noise interference. To address this, this paper proposes a brain-inspired forward-looking sonar target recognition framework named YOLO-VIS. We designed a low-level feature enhancement module based on large-kernel convolutions, which simulates the human brain’s preliminary processing of images by expanding the receptive field, thereby improving the quality of feature extraction. In addition, a visual attention weighting module is proposed, which further enhances the model’s focus on key features by optimizing feature selection based on the importance of neurons. Finally, through a multi-scale feature deep fusion module, the model’s target recognition capability at different scales is improved. Experimental results show that YOLO-VIS significantly improves target detection accuracy over existing methods on public datasets, verifying the effectiveness of brain-inspired mechanisms in sonar image recognition.