Loss Function Optimization Method and Unsupervised Extraction Approach D-DBSCAN for Improving the Moving Target Perception of 3D Imaging Sonar
Abstract
:1. Introduction
2. Data Collection and Annotation Method
2.1. Data Collection
2.2. Data Annotation
3. Loss Function Optimization and Semantic Segmentation Task
3.1. Loss Function
- is the loss weight for point ;
- is the local density of point , estimated using the nearest neighbor distance;
- is a hyperparameter controlling the weight variation, typically , to emphasize sparse regions.
- N is the total number of points in the point cloud;
- is the basic loss function (e.g., cross-entropy or mean squared error) for the true class and predicted class of point ;
- is the weight for point , emphasizing sparse regions. This density-weighted loss function reduces the risk of overfitting in dense regions by assigning lower weights while improving segmentation accuracy in sparse regions by assigning higher weights.
3.2. Semantic Segmentation Experiment
4. Unsupervised Inter-Frame Association Extraction Method D-DBSCAN and Target Detection Task
4.1. D-DBSCAN
4.2. Object Detection Task
5. Conclusions and Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Feature | CODA Specification |
---|---|
Frequency | 375, 610 kHz |
Beam Count | 128 × 128 (16,384 total) |
Field of View Coverage | 50° × 24°, 24° × 50° (standard) |
Pressure Depth | 600–3000 m (1968–9842 ft) |
Sonar Dimensions | 380 × 300 × 160 mm (15 × 12 × 6 in) |
Sonar Weight | 24.6 kg/54.2 lb |
Method | Lakebed | Noise | Tire | Mannequin | Table | mIOU |
---|---|---|---|---|---|---|
Baseline | 98.520 | 27.913 | 0.000 | 70.924 | 10.948 | 41.661 |
OURS | 99.280 | 30.052 | 0.283 | 78.735 | 34.698 | 48.6 (+6.939) |
Category | PointRCNN | OURS |
---|---|---|
Tire | 25.165 | 48.210 |
Mannequin | 81.810 | 93.431 |
Table | 10.214 | 39.525 |
mAP | 39.063 | 60.389 (+21.3) |
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Yu, J.; Huang, A.; Sun, Z.; Huang, R.; Huang, G.; Zhao, Q. Loss Function Optimization Method and Unsupervised Extraction Approach D-DBSCAN for Improving the Moving Target Perception of 3D Imaging Sonar. J. Mar. Sci. Eng. 2025, 13, 529. https://doi.org/10.3390/jmse13030529
Yu J, Huang A, Sun Z, Huang R, Huang G, Zhao Q. Loss Function Optimization Method and Unsupervised Extraction Approach D-DBSCAN for Improving the Moving Target Perception of 3D Imaging Sonar. Journal of Marine Science and Engineering. 2025; 13(3):529. https://doi.org/10.3390/jmse13030529
Chicago/Turabian StyleYu, Jingfeng, Aigen Huang, Zhongju Sun, Rui Huang, Gao Huang, and Qianchuan Zhao. 2025. "Loss Function Optimization Method and Unsupervised Extraction Approach D-DBSCAN for Improving the Moving Target Perception of 3D Imaging Sonar" Journal of Marine Science and Engineering 13, no. 3: 529. https://doi.org/10.3390/jmse13030529
APA StyleYu, J., Huang, A., Sun, Z., Huang, R., Huang, G., & Zhao, Q. (2025). Loss Function Optimization Method and Unsupervised Extraction Approach D-DBSCAN for Improving the Moving Target Perception of 3D Imaging Sonar. Journal of Marine Science and Engineering, 13(3), 529. https://doi.org/10.3390/jmse13030529