A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
Source abstract
Abstract
Because of their low cost and ease of deployment, side-scan sonars is one of the most widely used underwater survey instruments. However, the complexity of the marine environment and the difficulty in target acquisition limit the detection accuracy of side-scan sonars. To address these issues, this study proposes a small-sample target detection method of side-scan sonar images using the Cycle-Consistent Generative Adversarial Network (CycleGAN) model and the improved YOLOv8 model. First, considering the difficulty in obtaining side-scan sonar target images, the proposed method uses the CycleGAN model to generate pseudo-side-scan sonar images from optical images for data augmentation. Second, the original YOLOv8 model is revised by adding attention mechanisms, employing deformable convolution networks, and updating the loss function to improve the target detection accuracy of side-scan sonar images. Experimental results demonstrated the effectiveness of the CycleGAN model in generating pseudo-side-scan sonar images and better performance of the improved YOLOv8 model in the target detection of side-scan images. Moreover, the combination of data augmentation and the improved YOLOv8 model can remarkably increase the target detection accuracy of side-scan images. The proposed methods can effectively improve the target detection efficiency of underwater sonars in marine surveying.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image Processing Techniques and Applications · Advanced Neural Network Applications · Advanced Image Processing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
College of Civil Engineering, Anhui Jianzhu University, Hefei 230601, China
Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Anhui Province · University Natural Science Research Key Project of Anhui Province · University Synergy Innovation Program of Anhui Province · Key Research and Development Project of Anhui Province · Natural Science Residual Fund Project of Anhui Jianzhu University
References (36)
Show source-provided references
1. Target detection using features for sonar images10.1049/iet-rsn.2020.0224 ↗
2. Markov random field and fuzzy logic modeling in sonar imagery: Application to the classification of underwater floor10.1006/cviu.2000.0844 ↗
3. Classification and localization of naval mines with superellipse active contours
4. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.
5. Wang, Y., Liu, J., Yu, S., Wang, K., Han, Z., and Tang, Y. (2021, January 15–17). Underwater Object Detection based on YOLO-v3 network. Proceedings of the 2021 IEEE International Conference on Unmanned Systems (ICUS), Beijing, China.10.1109/ICUS52573.2021.9641489 ↗
6. A novel sonar target detection and classification algorithm10.1007/s11042-022-12054-4 ↗
7. Yu, Y., Zhao, J., Gong, Q., Huang, C., Zheng, G., and Ma, J. (2021). Real-time underwater maritime object detection in side-scan sonar images based on transformer-YOLOv5. Remote Sens., 13.10.3390/rs13183555 ↗
8. Zhang, F., Zhang, W., Cheng, C., Hou, X., and Cao, C. (2023). Detection of small objects in side-scan sonar images using an enhanced YOLOv7-based approach. J. Mar. Sci. Eng., 11.10.3390/jmse11112155 ↗
9. Underwater sonar target detection based on improved ScEMA YOLOv810.1109/LGRS.2024.3397848 ↗
10. Realistic sonar image simulation using deep learning for underwater object detection10.1007/s12555-019-0691-3 ↗
11. Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22–29). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.244 ↗
12. CycleGAN-based realistic image dataset generation for forward-looking sonar10.1080/01691864.2021.1873845 ↗
13. Sonar image generation by MFA-CycleGAN for boosting underwater object detection of AUVs10.1109/JOE.2024.3350746 ↗
14. Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 22–25). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.632 ↗
15. Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., and Wang, Z. (2017, January 22–25). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.19 ↗
16. Vats, A., and Anastasiu, D.C. (2023, January 18–22). Enhancing retail checkout through video inpainting, yolov8 detection, and deepsort tracking. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPRW59228.2023.00585 ↗
17. Feng, C., Zhong, Y., Gao, Y., Scott, M.R., and Huang, W. (2021, January 10–17). Tood: Task-aligned one-stage object detection. Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.10.1109/ICCV48922.2021.00349 ↗
18. Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection
19. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7–12). Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i07.6999 ↗
20. Liu, Y., Shao, Z., and Hoffmann, N. (2021). Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv.
21. Wang, W., Dai, J., Chen, Z., Huang, Z., Li, Z., Zhu, X., and Li, H. (2023, January 18–22). Internimage: Exploring large-scale vision foundation models with deformable convolutions. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.01385 ↗
22. Focal and efficient IOU loss for accurate bounding box regression10.1016/j.neucom.2022.07.042 ↗
23. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
24. Zhu, X., Hu, H., Lin, S., and Dai, J. (2019, January 16–20). Deformable convnets v2: More deformable, better results. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00953 ↗
25. Girshick, R. (2015). Fast r-cnn. arXiv.10.1109/ICCV.2015.169 ↗
26. Alpha-IoU: A family of power intersection over union losses for bounding box regression
27. Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 16–20). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00075 ↗
28. Yu, J., Jiang, Y., Wang, Z., Cao, Z., and Huang, T. (2016, January 15–19). Unitbox: An advanced object detection network. Proceedings of the 24th ACM International Conference on Multimedia, New York, NY, USA.10.1145/2964284.2967274 ↗
29. Li, Y., Liu, M.Y., Li, X., Yang, M.H., and Kautz, J. (2018, January 8–14). A closed-form solution to photorealistic image stylization. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01219-9_28 ↗
30. Liu, S., Lin, T., He, D., Li, F., Wang, M., Li, X., and Ding, E. (2021, January 10–17). Adaattn: Revisit attention mechanism in arbitrary neural style transfer. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada.10.1109/ICCV48922.2021.00658 ↗
31. Huang, X., and Belongie, S. (2017, January 22–29). Arbitrary style transfer in real-time with adaptive instance normalization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.167 ↗
32. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23–28). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.10.1007/978-3-030-58452-8_13 ↗
33. Wang, C.-Y., Yeh, I.-H., and Liao, H.Y.M. (2024). Yolov9: Learning what you want to learn using programmable gradient information. arXiv.10.1007/978-3-031-72751-1_1 ↗
34. Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). Yolov10: Real-time end-to-end object detection. arXiv.
35. Khanam, R., and Hussain, M. (2024). Yolov11: An overview of the key architectural enhancements. arXiv.
36. A dataset with multibeam forward-looking sonar for underwater object detection10.1038/s41597-022-01854-w ↗