A Spatial Distribution Probability-Guided Detection Framework for Underwater Sonar Imagery.
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| Title: | A Spatial Distribution Probability-Guided Detection Framework for Underwater Sonar Imagery. |
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| Authors: | Jia, Dayu1 (AUTHOR), Huang, Yan1,2 (AUTHOR), Qiao, Jianan1,2 (AUTHOR), Wang, Zhenyu1,2 (AUTHOR), Feng, Hao1 (AUTHOR), Yu, Jiancheng1 (AUTHOR) yjc@sia.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1906. 21p. |
| Subjects: | Sonar imaging, Distribution (Probability theory), Object recognition (Computer vision), Machine learning, Remote submersibles, Artificial neural networks |
| Abstract: | Highlights: What are the main findings? A real sonar dataset containing practice mines and practice subsurface buoys is created. The dataset is used to train and validate a deep neural network. A Spatial Distribution Probability-Guided Detection Framework is designed, focusing on submarine point target detection, which achieves superior detection performance. What are the implications of the main findings? Spatial distribution probability provides reliable prior knowledge which enables the deep neural network to focus on the target part. The proposed framework achieves robust object detection in data-scarce scenarios, demonstrating generalizability beyond underwater sonar datasets. Underwater target detection via side-scan sonar is vital for defense and economy but hindered by sparse targets, high data costs, and feature extraction difficulties due to textureless acoustic data and limited samples. To overcome these limitations, particularly for few-shot, small-object detection, we propose a Spatial Distribution Probability-Guided Detection Framework to aid Unmanned Underwater Vehicles (UUVs) in precise localization and clustering. The framework features a novel module that leverages a pre-trained Vision Foundation Model (DINOv3) to generate spatial distribution probability maps, guiding a Transformer-based network for accurate detection with scarce data. Additionally, it incorporates a Target Position Calculation Module and a DBSCAN-based post-processing module to determine global geographic coordinates and cluster discrete points, respectively. Experiments were conducted on both a Public Mine Detection Dataset and a self-collected dataset containing simulated mines and buoys. Ablation studies and comparison experiments demonstrated that the proposed guidance mechanism significantly improves detection performance. Furthermore, two comb-search missions verified that the system could accurately locate and cluster targets, distinguishing real targets from false detections (noise). These results confirm the framework's efficacy in enabling high-precision perception and autonomous operations for complex underwater inspection tasks. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A real sonar dataset containing practice mines and practice subsurface buoys is created. The dataset is used to train and validate a deep neural network. A Spatial Distribution Probability-Guided Detection Framework is designed, focusing on submarine point target detection, which achieves superior detection performance. What are the implications of the main findings? Spatial distribution probability provides reliable prior knowledge which enables the deep neural network to focus on the target part. The proposed framework achieves robust object detection in data-scarce scenarios, demonstrating generalizability beyond underwater sonar datasets. Underwater target detection via side-scan sonar is vital for defense and economy but hindered by sparse targets, high data costs, and feature extraction difficulties due to textureless acoustic data and limited samples. To overcome these limitations, particularly for few-shot, small-object detection, we propose a Spatial Distribution Probability-Guided Detection Framework to aid Unmanned Underwater Vehicles (UUVs) in precise localization and clustering. The framework features a novel module that leverages a pre-trained Vision Foundation Model (DINOv3) to generate spatial distribution probability maps, guiding a Transformer-based network for accurate detection with scarce data. Additionally, it incorporates a Target Position Calculation Module and a DBSCAN-based post-processing module to determine global geographic coordinates and cluster discrete points, respectively. Experiments were conducted on both a Public Mine Detection Dataset and a self-collected dataset containing simulated mines and buoys. Ablation studies and comparison experiments demonstrated that the proposed guidance mechanism significantly improves detection performance. Furthermore, two comb-search missions verified that the system could accurately locate and cluster targets, distinguishing real targets from false detections (noise). These results confirm the framework's efficacy in enabling high-precision perception and autonomous operations for complex underwater inspection tasks. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121906 |
