Side-Scan Sonar Image Classification Based on Style Transfer and Pre-Trained Convolutional Neural Networks
Open accessAbstract
Side-scan sonar is widely used in underwater rescue and the detection of undersea targets, such as shipwrecks, aircraft crashes, etc. Automatic object classification plays an important role in the rescue process to reduce the workload of staff and subjective errors caused by visual fatigue. However, the application of automatic object classification in side-scan sonar images is still lacking, which is due to a lack of datasets and the small number of image samples containing specific target objects. Secondly, the real data of side-scan sonar images are unbalanced. Therefore, a side-scan sonar image classification method based on synthetic data and transfer learning is proposed in this paper. In this method, optical images are used as inputs and the style transfer network is employed to simulate the side-scan sonar image to generate “simulated side-scan sonar images”; meanwhile, a convolutional neural network pre-trained on ImageNet is introduced for classification. In this paper, we experimentally demonstrate that the maximum accuracy of target classification is up to 97.32% by fine-tuning the pre-trained convolutional neural network using a training set incorporating “simulated side-scan sonar images”. The results show that the classification accuracy can be effectively improved by combining a pre-trained convolutional neural network and “similar side-scan sonar images”.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A VGG-19 pretrained on ImageNet and fine-tuned with a synthetic dataset generated by style transfer achieved over 97% accuracy in the classification of real sonar images.
“This work achieved an accuracy of over 97% in the classification of real sonar images for the CNNs that were fine-tuned with the synthetically generated dataset”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [132]
The Abstract reports a maximum accuracy of 97.32% after fine-tuning an ImageNet-pretrained CNN with a training set containing style-transfer-generated sonar images. Section 4.2.3 says the test images were real, but Table 2 lists the transferred VGG19 model’s five-run average at 96.88%, not over 97%; the result also does not apply to all the CNNs tested. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA model trained with synthetic data generated by neural style transfer can improve both classification accuracy and convergence time.
“The data were used to balance an uneven dataset, verifying that a model trained with synthetic data can improve both the classification accuracy and the convergence time.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [132]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Image Style Transfer Using Convolutional Neural Networks
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.265 · Cited by 6,025 · see citing works
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150 · see citing works
A Neural Algorithm of Artistic Style
Gatys, L., Ecker, A., & Bethge, M. (2016) · Journal of Vision · doi:10.1167/16.12.326 · Cited by 2,612 · see citing works
Understanding Convolution for Semantic Segmentation
Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., & Cottrell, G. W. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00163 · Cited by 2,127 · see citing works
Hertzmann, A., Jacobs, C. E., Oliver, N., Curless, B., & Salesin, D. (2001) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/383259.383295 · Cited by 1,579 · see citing works
Zaki, M. J., & Meira, W. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810114 · Cited by 1,156 · see citing works
Texture Synthesis Using Convolutional Neural NetworksPreprint
Gatys, L. A., Ecker, A. S., & Bethge, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.07376 · Cited by 902 · see citing works
Data Mining and Analysis: Fundamental Concepts and Algorithms
Zaki, M. J., & Meira, W. (2014) · Cited by 780 · see citing works
Winnemöller, H., Olsen, S. C., & Gooch, B. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1142018 · Cited by 539 · see citing works
Huo, G., Wu, Z., & Li, J. (2020) · IEEE Access · doi:10.1109/access.2020.2978880 · Cited by 241 · see citing works
An automatic approach to the detection and extraction of mine features in sidescan sonar
Reed, S., Pétillot, Y., & Bell, J. (2003) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2002.808199 · Cited by 240 · see citing works
Williams, D. P. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900011 · Cited by 119 · see citing works
Ashikhmin, M. (2003) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2003.1210863 · Cited by 114 · see citing works
Sonar image segmentation based on GMRF and level-set models
Ye, X.-F., Zhang, Z., Liu, P. X., & Guan, H.-L. (2010) · Ocean Engineering · doi:10.1016/j.oceaneng.2010.03.003 · Cited by 91 · see citing works
Lee, H., Seo, S., Ryoo, S.-T., & Yoon, K. (2010) · International Symposium on Non-Photorealistic Animation and Rendering (NPAR) · doi:10.1145/1809939.1809945 · Cited by 78 · see citing works
Weld Defect Images Classification with VGG16-Based Neural Network
Liu, B., Zhang, X., Gao, Z., & Chen, L. (2018) · Communications in computer and information science · doi:10.1007/978-981-10-8108-8_20 · Cited by 60 · see citing works
Development and validation of a deep learning system for ascites cytopathology interpretation
Su, F., Sun, Y., Hu, Y., Yuan, P., Wang, X., Wang, Q., Li, J., & Ji, J. (2020) · Gastric Cancer · doi:10.1007/s10120-020-01093-1 · Cited by 51 · see citing works
Kumar, N., Mitra, U., & Narayanan, S. S. (2014) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2014.2344971 · Cited by 47 · see citing works
The convolution neural network based agent vehicle detection using forward-looking sonar image
Kim, J., Cho, H., Pyo, J., Kim, B.-J., & Yu, S. (2016) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans.2016.7761209 · Cited by 36 · see citing works
Key, W. H. (2002) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans.2000.881735 · Cited by 32 · see citing works
PCA and Kernel-based extreme learning machine for side-scan sonar image classification
Zhu, M., Song, Y., Guo, J. Q., Feng, C., Li, G., Yan, T., & He, B. (2017) · IEEE Underwater Technology (UT) · doi:10.1109/ut.2017.7890275 · Cited by 29 · see citing works
Comparative study of information fusion methods for sonar images classification
Arnaud Martin, B. (2005) · International Conference on Information Fusion (FUSION) · doi:10.1109/icif.2005.1592022 · Cited by 28 · see citing works
Sinai, A., Amar, A., & Gilboa, G. (2016) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans.2016.7760991 · Cited by 23 · see citing works
Quidu, I., Malkasse, J. P., Burel, G., & Vilbé, P. (2002) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans.2000.881274 · Cited by 19 · see citing works
Klein, M. (2002) · Springer eBooks · doi:10.1007/978-1-4615-0535-8_39 · Cited by 18 · see citing works
Mine-Like Object Sensing in Sonar Imagery with a Compact Deep Learning Architecture for Scarce Data
Phung, S. L., Nguyen, T. N. A., Le, H. T., Chapple, P. B., Ritz, C. H., Bouzerdoum, A., & Tran, L. C. (2019) · 2019 Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta47822.2019.8945982 · Cited by 17 · see citing works
Klaucke, I. (2017) · Springer geology · doi:10.1007/978-3-319-57852-1_2 · Cited by 17 · see citing works
Sonar image segmentation based on statistical modeling of wavelet subbands
Karine, A., Lasmar, N.-E., Baussard, A., & Hassouni, M. E. (2015) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2015.7507134 · Cited by 11 · see citing works
Sadjadi, F. A. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2183138 · Cited by 7 · see citing works
Study of Side Scan Sonar Image Classification Based on SVM and Gray Level Co-Cccurrence Matrix
Guo, J. (2015) · Geomatics & Spatial Information Technology · Cited by 3 · see citing works
Underwater object highlight segmentation in SAS image using Rayleigh mixture model
Zhai, H., Jiang, Z., Zhang, P., Tian, J., & Liu, J. (2015) · IEEE International Conference on Control System, Computing and Engineering · doi:10.1109/iccsce.2015.7482222 · Cited by 2 · see citing works
A Neural Algorithm of Artistic Style
Gatys, L. A., Ecker, A. S., & Bethge, M. (2015) · arXiv · doi:10.48550/arxiv.1508.06576
Flagged first, then by citations
Research works printed but not linked: 3
- Klaucke, I. Side Scan Sonar. In Submarine Geomorphology; Springer: Cham, Switzerland, 2018; pp. 13-24.
- Dobeck, G.J.; Hyland, J.C. Automated detection and classification of sea mines in sonar imagery. Detection and Remediation Technologies for Mines and Minelike Targets II. Int. Soc. Opt. Photonics 1997, 3079, 90-110.
- Subetha, T.; Khilar, R.; Christo, M.S. A comparative analysis on plant pathology classification using deep learning architecture- Resnet and VGG19. Mater. Today Proc. 2021. [CrossRef] · DOI
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
Real-time underwater target detection for AUV using side scan sonar images based on deep learning
Li, L., Li, Y., Yue, C., Xu, G., Wang, H., & Feng, X. (2023) · Applied Ocean Research · doi:10.1016/j.apor.2023.103630 · Cited by 88 · see citing works
Yang, S., Jin, A., Zeng, X., Wang, H., Hong, X., & Lei, M. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107983 · Cited by 55 · see citing works
Cheng, Z., Huo, G., & Li, H. (2022) · Remote Sensing · doi:10.3390/rs14020355 · Cited by 51 · see citing works
Sonar Image Generation by MFA-CycleGAN for Boosting Underwater Object Detection of AUVs
Zhou, J., Li, Y., Qin, H., Dai, P., Zhao, Z., & Hu, M. (2024) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2024.3350746 · Cited by 39 · see citing works
Multi-Scale Marine Object Detection in Side-Scan Sonar Images Based on BES-YOLO
Ma, Q., Jin, S., Bian, G., & Cui, Y. (2024) · Sensors · doi:10.3390/s24144428 · Cited by 30 · see citing works
A Side-Scan Sonar Image Synthesis Method Based on a Diffusion Model
Yang, Z., Zhao, J., Zhang, H., Yu, Y., & Huang, C. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11061103 · Cited by 29 · see citing works
Grządziel, A. (2023) · Remote Sensing · doi:10.3390/rs15235599 · Cited by 22 · see citing works
Underwater Rescue Target Detection Based on Acoustic Images
Hu, S., & Liu, T. (2024) · Sensors · doi:10.3390/s24061780 · Cited by 22 · see citing works
Liu, X., Zhu, H., Song, W., Wang, J., Yan, L., & Wang, K. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3459928 · Cited by 21 · see citing works
Multi-Modal Multi-Stage Underwater Side-Scan Sonar Target Recognition Based on Synthetic Images
Wang, J., Li, H., Huo, G., Li, C., & Wei, Y. (2023) · Remote Sensing · doi:10.3390/rs15051303 · Cited by 21 · see citing works
Yang, Z., Zhao, J., Yu, Y., & Huang, C. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3371051 · Cited by 19 · see citing works
An Image Enhancement Method for Side-Scan Sonar Images Based on Multi-Stage Repairing Image Fusion
Lu, Z., Zhu, T., Zhou, H., Zhang, L., & Jia, C. (2023) · Electronics · doi:10.3390/electronics12173553 · Cited by 18 · see citing works
Improving Yolo5 for Real-Time Detection of Small Targets in Side Scan Sonar Images
Wang, J., Wang, Q., Gao, G., Qin, P., & He, B. (2023) · Journal of Ocean University of China · doi:10.1007/s11802-023-5444-5 · Cited by 15 · see citing works
Sample Augmentation Method for Side-Scan Sonar Underwater Target Images Based on CBL-sinGAN
Peng, C., Jin, S., Bian, G., Cui, Y., & Wang, M. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12030467 · Cited by 13 · see citing works
Side-Scan Sonar Image Augmentation Method Based on CC-WGAN
Zhu, J., Li, H., Qing, P., Hou, J., & Peng, Y. (2024) · Applied Sciences · doi:10.3390/app14178031 · Cited by 13 · see citing works
Target Recognition and Detection in Side-Scan Sonar Images based on YOLO v3 Model
Li, J., & Cao, X. S. (2022) · Chinese Control Conference (CCC) · doi:10.23919/ccc55666.2022.9902742 · Cited by 12 · see citing works
Tang, R., Chen, Y., Gao, D., Gao, J., & Hao, S. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.123771 · Cited by 10 · see citing works
Ge, Q., Liu, H., Ma, Y., Han, D., Zuo, X., & Dang, L. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06227-1 · Cited by 10 · see citing works
Self-citationDu, X., Sun, Y., Song, Y., Chi, W., Dong, L., & Zhao, X. (2025) · Remote Sensing · doi:10.3390/rs17142431 · Cited by 10 · see citing works
Dual-Path Residual “Shrinkage” Network for Side-Scan Sonar Image Classification
Ruan, F., Dang, L., Ge, Q., Zhang, Q., Qiao, B., & Zuo, X. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/6962838 · Cited by 10 · see citing works
Self-citationSide-Scan Sonar Image Generator Based on Diffusion Models for Autonomous Underwater Vehicles
Zhang, F., Hou, X., Wang, Z., Cheng, C., & Tan, T. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12081457 · Cited by 8 · see citing works
Nga, Y. Z., Rymansaib, Z., Treloar, A. A., & Hunter, A. J. (2024) · Remote Sensing · doi:10.3390/rs16214036 · Cited by 8 · see citing works
Du, X., Sun, Y., Song, Y., Dong, L., Tao, C., & Wang, D. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13030424 · Cited by 8 · see citing works
Kubicek, B., Gupta, A. S., & Kirsteins, I. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11030571 · Cited by 7 · see citing works
Zhu, J., Cai, W., Zhang, M., & Pan, M. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3356712 · Cited by 7 · see citing works
Wang, J., Li, H., Dong, C., Wang, J., Zheng, B., & Xing, T. (2023) · Remote Sensing · doi:10.3390/rs15184517 · Cited by 6 · see citing works
Wang, Y., Chen, Y., Yu, D., & Zhang, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3614197 · Cited by 5 · see citing works
Adversarial enhancement generation method for side-scan sonar images based on DDPM–YOLO
Peng, C., Jin, S., Liu, H., Zhang, W., & Xia, H. (2024) · Marine Geodesy · doi:10.1080/01490419.2024.2393190 · Cited by 5 · see citing works
Acoustic Imaging Learning-Based Approaches for Marine Litter Detection and Classification
Guedes, P. A., Silva, H. M., Wang, S., Martins, A., Almeida, J. M., & Silva, E. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12111984 · Cited by 5 · see citing works
Xie, B., Zhang, H., & Wang, W. (2025) · Electronics · doi:10.3390/electronics14071287 · Cited by 4 · see citing works
Cycle-GAN-based synthetic sonar image generation for improved underwater classification
Koo, S., Youm, S., & Shin, J. (2024) · SPIE Ocean Sensing and Monitoring · doi:10.1117/12.3016056 · Cited by 4 · see citing works
DBnet: A Lightweight Dual-Backbone Target Detection Model Based on Side-Scan Sonar Images
Ma, Q., Jin, S., Bian, G., Cui, Y., & Liu, G. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13010155 · Cited by 3 · see citing works
Wei, M., Yu, Y., Du, X., Song, Y., Dong, L., Zhou, Q., Wang, L., Zhang, L., & Wang, Y. (2025) · Frontiers in Earth Science · doi:10.3389/feart.2025.1596238 · Cited by 3 · see citing works
Kamal Basha, S., Anukul Kiran, B., Nambiar, A., & Rajendran, S. (2025) · doi:10.23919/oceans59106.2025.11244959 · Cited by 3 · see citing works
Classification Method of Traditional Art Painting Style Based on Color Space Transformation
Xu, Z. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01411102 · Cited by 2 · see citing works
Deep Learning for Detection of Underwater Aircraft Wrecks from US Conflicts
Character, L., Moline, M. A., Breece, M., White, E., Davis, D., & Colbourn, C. (2025) · Journal of Computer Applications in Archaeology · doi:10.5334/jcaa.179 · Cited by 2 · see citing works
Saenko, A., Mironov, A., & Fomina, E. (2024) · International Russian Automation Conference (RusAutoCon) · doi:10.1109/rusautocon61949.2024.10694680 · Cited by 1 · see citing works
Sonar images generation method based on improved stable diffusion combined with ControlNet model
希吉尔, Ye, X., Guo, S., Li, C., & Huang, H. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02334-6 · Cited by 1 · see citing works
Multi-channel input augmentation for sonar image–based recognition of drowned victims
Jaszcz, A., Wawrzyniak, N., Połap, D., Zaniewicz, G., & Prokop, K. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134143 · Cited by 1 · see citing works
Diffusion-Model-Based Data Augmentation for Target Detection in Side-Scan Sonar Images
Yang, Y., & Zhang, T. (2026) · Remote Sensing · doi:10.3390/rs18132193 · Cited by 1 · see citing works
Su, D., Huang, Y., Yang, F., Zhao, D., Yang, A., & Jiaoyang, L. I. U. (2023) · National Remote Sensing Bulletin · doi:10.11834/jrs.20222283 · Cited by 1 · see citing works
Laser Triangulation and Optical Computed Tomography 3D Scanner
Estrellado, V. L. P., & Cruz, J. C. D. (2025) · IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) · doi:10.1109/sceecs64059.2025.10940200 · Cited by 1 · see citing works
Adaptive Style Transfer Method of Art Works Based on Laplace Operator
Jia, H. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140711
Recognition of side-scan sonar images under long-tail distribution
Yang, Y., Yi, W., Guo, J., Qiu, H., Cao, Z., Zhang, S., & Zhang, J. (2025) · International Conference on Computer Vision and Image Processing (CVIP) · doi:10.1117/12.3058318
Geng, J. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-9128-6_8
Dhaneshwar, R., Kaur, M., Kaur, M., & Gupta, V. (2025) · Journal of Forensic Sciences · doi:10.1111/1556-4029.70204
Kamal Basha, S., Anukul Kiran, B., Nambiar, A., Rajendran, S., & Ambat, S. K. (2026) · The Journal of the Acoustical Society of America · doi:10.1121/10.0042386
Wang, X., Zhang, Y., 吴宪霞, & Tian, F. (2026) · Journal of Real-Time Image Processing · doi:10.1007/s11554-026-01932-x
- Authors
- Ge, Q., Ruan, F., Qiao, B., Zhang, Q., Zuo, X., & Dang, L.
- Affiliations
- Henan University (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Dang, Lanxue
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2021-07-29
- Volume
- 10
- Issue
- 15
- Pages
- 1823
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W3189916153
- Cited by
- 50
- References
- 32
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 17 · 2023: 11 · 2022: 4
- FWCI
- 3.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Basic Research Program of China;the Major Project of Science and Technology of Henan Province;National Natural Science Foundation of China;Key Research and Promotion Projects of Henan Province (2019YFE0126600;201400210300;U1704122;212102210393, 202102110121)
- 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 › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- side-scan sonar images, underwater target classification, style transfer, synthetic image generation, transfer learning, pre-trained convolutional neural networks, class imbalance, underwater rescue, shipwreck detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
