Deep learning applied to underwater mine warfare
Abstract
In this article we are addressing the problem of automatic detection and classification of underwater mines on images generated by a Synthetic Aperture Sonar (SAS). To tackle this problem, we are investigating the use of Machine Learning techniques, in particular Deep Learning. Using this method we faced two challenges, (i) the availability of a sufficient amount of training data to learn the classification model and (ii) the design of the deep learning pipeline suited for this one-class classification problem. Our contributions in this paper are, first the synthetic generation of realistic image datasets for the training of our Machine Learning algorithm, and second the research and development of a novel Deep Learning approach for automatic underwater mines classification using sonar images. The combination of these two contributions offers a new pipeline of operation for Mine Counter Measure Automatic Target Recognition (MCM ATR) systems.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Using autoencoders to extract mines before applying a convolutional neural network reduced the number of examples needed to train the CNN.
“Although the results presented consider only computer-generated cylindrical mines, the use of autoencoders to extract mines before the application of a convolutional neural network reduced the amount of examples needed to train the latter.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [124]
The presence of sonar echo generated from the mine itself can mask the real shape of the mine-like object, but it had a minor influence on learning the shadow concept.
“The presence of sonar echo generated from the mine itself can mask the real shape of the MLO, but it had a minor influence on learning the shadow concept [124].”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [124]
Much work in underwater acoustic data classification is conducted using synthetic data only or on a limited set of real data augmented with semisynthetic examples for training.
“Therefore, much work in this area is conducted using synthetic data only [124], or on a limited set of real data augmented with semisynthetic examples for training [62,128,139].”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [124]
The main limitation of the method in [124] was difficulty in representing the sonar echo, which was ignored in the reported experiments.
“The main limitation of this method was the difficulty in representing the sonar echo, which was ignored on the experiments reported.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [124]
Cited by
Sorted by citations
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
“The detection of underwater MLOs from synthetically generated data was attempted in [124], using a two-stage process.”
BackgroundMethodInfluentialCrosstalk Removal in Forward Scan Sonar Image Using Deep Learning for Object Detection
Sung, M., Cho, H., Kim, T., Joe, H., & Yu, S.-C. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2925830 · Cited by 43 · see citing works
- Authors
- Denos, K., Ravaut, M., Fagette, A., & Lim, H.-S.
- ORCID
- Ravaut, Mathieu
- Venue
- OCEANS 2017 - Aberdeen
- Publisher
- IEEE
- Published
- 2017-06
- Pages
- 1-7
- OpenAlex
- W2765509090
- Cited by
- 59
- References
- 0
- Citations by year
- 2025: 8 · 2024: 6 · 2023: 9 · 2022: 11 · 2021: 5 · 2020: 8 · 2019: 7 · 2018: 5
- FWCI
- 38.53
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- MTS/IEEE OCEANS Conference (OCEANS) (publishedVersion)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
- Physical Sciences › Engineering › Aerospace Engineering › Advanced SAR Imaging Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- underwater mine detection, synthetic aperture sonar, sonar images, mine countermeasures, automatic target recognition, one-class classification, synthetic image generation, training data generation, deep learning pipeline
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