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...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2510568 |
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| author | Xi Zhao Qiangqiang Yuan Jiadan Xu |
| author_facet | Xi Zhao Qiangqiang Yuan Jiadan Xu |
| author_sort | Xi Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c5a934ebca8c427e870960b799064b6d |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-c5a934ebca8c427e870960b799064b6d2025-08-25T11:31:54ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2510568Efficient processing of side-scan sonar images and fast detection of sparse targets in large-scale imagesXi Zhao0Qiangqiang Yuan1Jiadan Xu2School of Geodesy and Geomatics, Wuhan University, Wuhan, People’s Republic of ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan, People’s Republic of ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan, People’s Republic of ChinaPoor 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.https://www.tandfonline.com/doi/10.1080/17538947.2025.2510568Acoustic imageefficient image processingdeep learningautomatic target recognitionside-scan sonar |
| spellingShingle | Xi Zhao Qiangqiang Yuan Jiadan Xu Efficient processing of side-scan sonar images and fast detection of sparse targets in large-scale images International Journal of Digital Earth Acoustic image efficient image processing deep learning automatic target recognition side-scan sonar |
| title | Efficient processing of side-scan sonar images and fast detection of sparse targets in large-scale images |
| title_full | Efficient processing of side-scan sonar images and fast detection of sparse targets in large-scale images |
| title_fullStr | Efficient processing of side-scan sonar images and fast detection of sparse targets in large-scale images |
| title_full_unstemmed | Efficient processing of side-scan sonar images and fast detection of sparse targets in large-scale images |
| title_short | Efficient processing of side-scan sonar images and fast detection of sparse targets in large-scale images |
| title_sort | efficient processing of side scan sonar images and fast detection of sparse targets in large scale images |
| topic | Acoustic image efficient image processing deep learning automatic target recognition side-scan sonar |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2510568 |
| work_keys_str_mv | AT xizhao efficientprocessingofsidescansonarimagesandfastdetectionofsparsetargetsinlargescaleimages AT qiangqiangyuan efficientprocessingofsidescansonarimagesandfastdetectionofsparsetargetsinlargescaleimages AT jiadanxu efficientprocessingofsidescansonarimagesandfastdetectionofsparsetargetsinlargescaleimages |