Efficient processing of side-scan sonar images and fast detection of sparse targets in large-scale images

Poor feature representation, confusing background topography, and excessive data volume render detecting sparse targets in large-size acoustic imagery challenging. Especially when conducting real-time processing tasks, accuracy and speed are required to be optimized with limited computational resources. Therefore, this paper proposes an efficient method for real-time side-scan sonar (SSS) image processing and detection of sparse targets in large-scale images. Primarily, an intelligent real-time processing method is proposed for the raw SSS data to acquire high-quality SSS images. Aiming at the characteristics of large-size SSS images and sparse targets, we propose an innovative two-stage inference method: The SSS image slices are pre-classified based on the MobileViTv3-XXS model, and then the optimized detection model of RepVGG+YOLOv5m is employed for target detection of image slices containing targets. Experiments show that real-time preprocessing yields SSS images with an average PSNR of 27.112 and SSIM of 0.816, comparable to the post-processing methods. Meanwhile, it maintains high efficiency and achieves 88.2% mAP, significantly outperforming the slice-only method in detection accuracy and efficiency.