Mine-Like Object Sensing in Sonar Imagery with a Compact Deep Learning Architecture for Scarce Data
Abstract
Detection of underwater mines is important for ensuring the safety of maritime routes. This paper presents a new approach for mine-like object sensing in sonar imagery. We propose a deep learning architecture that combines a convolution neural network and a hierarchical Gaussian process classifier. The proposed architecture is designed to improve the classification accuracy of the conventional convolutional neural network and to provide a well-calibrated measure of classification uncertainty. It can be trained in an end-to-end manner with labeled examples, or sonar snapshots, of underwater objects. To address the data scarcity in this application, we apply the generative adversarial network to produce extra sonar snapshots for training. Evaluated on a dataset of 349 sonar snapshots, the proposed method achieves an overall classification rate of 81.6%, which is significantly higher than the existing methods.
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A typical GAN applied to generate sonar images of mine-like objects produced only a moderate improvement in accuracy of a CNN+HGP classifier (1.36 percentage points, maximum accuracy 81.56%) and a similar improvement for a stand-alone CNN (56.5% to 57.2%).
“The results show only a moderate improvement in the accuracy of the CNN+HGP classifier when using the augmented dataset (1.36 percentage points, achieving a maximum accuracy of 81.56%); a similar improvement was observed with respect to a stand-alone CNN (an improvement from 56.5% without the data augmentation to 57.2% with the GAN-generated dataset).”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [147]
Cites
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471 · see citing works
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Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · Sensors · doi:10.3390/s22062181 · Cited by 93 · see citing works
“[147] 2019 GAN generated sonar images of MLO.”
BackgroundMethodGe, Q., Ruan, F., Qiao, B., Zhang, Q., Zuo, X., & Dang, L. (2021) · Electronics · doi:10.3390/electronics10151823 · Cited by 50 · see citing works
- Authors
- Phung, S. L., Nguyen, T. N. A., Le, H. T., Chapple, P. B., Ritz, C. H., Bouzerdoum, A., & Tran, L. C.
- Affiliations
- University of Wollongong (AU)
- Defence Science and Technology Group (AU)
- Venue
- 2019 Digital Image Computing: Techniques and Applications (DICTA)
- Publisher
- IEEE
- Published
- 2019-12
- Pages
- 1-7
- OpenAlex
- W2998259741
- Cited by
- 17
- References
- 3
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 3 · 2023: 2 · 2022: 3 · 2021: 3 · 2020: 1
- FWCI
- 10.80
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Digital Image Computing: Techniques and Applications (DICTA) (publishedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
- Keywords
- underwater mine detection, sonar images, convolutional neural networks, uncertainty calibration, generative adversarial networks, data augmentation, limited training data
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