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 resour...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2510568 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 1753-8947 1753-8955 |