Underwater Acoustic Target Classification Based on Dense Convolutional Neural Network
Abstract
In oceanic remote sensing operations, underwater acoustic target recognition is always a difficult and extremely important task of sonar systems, especially in the condition of complex sound wave propagation characteristics. The expensively learning recognition model for big data analysis is typically an obstacle for most traditional machine learning (ML) algorithms, whereas the convolutional neural network (CNN), a type of deep neural network, can automatically extract features for accurate classification. In this study, we propose an approach using a dense CNN model for underwater target recognition. The network architecture is designed to cleverly reuse all former feature maps to optimize classification rates under various impaired conditions while satisfying low computational cost. In addition, instead of using time–frequency spectrogram images, the proposed scheme allows directly utilizing the original audio signal in the time domain as the network input data. Based on the experimental results evaluated on the real-world data set of passive sonar, our classification model achieves the overall accuracy of 98.85% at 0-dB signal-to-noise ratio (SNR) and outperforms traditional ML techniques, as well as other state-of-the-art CNN models.
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UATC-DenseNet is composed of a deep CNN with dense architecture trained to recognise 12 classes of signals.
“This model is composed of a deep CNN with dense architecture that was trained to recognise 12 classes of signals.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [92]
The dataset used for UATC-DenseNet was labelled by a sonar expert, containing 11 different classes of sounds and one class corresponding to ambient sound.
“The dataset used was labelled by a sonar expert, containing 11 different classes of sounds and one class corresponding to the ambient sound.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [92]
UATC-DenseNet was designed to classify underwater acoustic targets from raw audio signals.
“Similarly, a CNN model called Underwater Acoustic Target Classification DenseNet (UATC-DenseNet) [92] was designed to classify underwater acoustic targets from raw audio signals.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [92]
A particular UATC-DenseNet architecture (six convolutional filters with kernel size 1 × 15), using original sound data without preprocessing layers or filters, outperformed other classical methods such as CNN-extreme learning machine, ResNet18, and SqueezeNet.
“The results reported show that a particular model architecture (with six convolutional filters with a kernel size of 1 × 15), using the original sound data as input without preprocessing layers or filters, outperformed other classical methods, such as CNN-extreme learning machine [91], ResNet18 [58] and SqueezeNet [97].”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [92]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · 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
“, 2020 [92] UATC-DenseNet Real-world passive sonar data b 98.”
BackgroundMethodDomingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · IEEE Access · doi:10.1109/access.2022.3220265 · Cited by 37 · see citing works
- Authors
- Doan, V.-S., Huynh-The, T., & Kim, D.-S.
- Affiliations
- Kumoh National Institute of Technology (KR)
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2022
- Volume
- 19
- Pages
- 1-5
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W3093961233
- Cited by
- 186
- References
- 1
- Citations by year
- 2026: 29 · 2025: 46 · 2024: 44 · 2023: 38 · 2022: 16 · 2021: 13
- FWCI
- 8.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Geoscience and Remote Sensing Letters (publishedVersion)
- Funders
- National Research Foundation (NRF), South Korea, through the International Cooperation Program (2019K2A9A1A09081533)
- NRF, South Korea, through the Priority Research Centers Program funded by the Ministry of Education, Science and Technology (2018R1A6A1A03024003)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
- Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- dense convolutional neural network, passive sonar, sonar target recognition, low signal-to-noise ratio, feature reuse
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