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Underwater Object Detection Using Deep Learning
Authors
1 Student, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India. 2Associate Professor, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India.
Article Information
- Published In
- Volume 5, Issue 4 (2025)
- Published Online
- July-August 2025
- Pages
- 29-33
- Article Type
- Original Article
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References
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13. Y. Jiang, X. Sun, and S. Liu, “Enhanced YOLOv5 for Real-Time Underwater Object Detection,” in Proc. Int. Conf. on Artificial Intelligence and Computer Engineering (ICAICE), 2021, pp. 123–128.
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16. M. Liu, X. Wu, and H. Xu, “Domain Adaptation for Underwater Object Detection Using Style Transfer,” in Proc. IEEE Int. Conf. Image Process. (ICIP), 2020, pp. 3511–3515.
17. H. Fang, Z. Zhang, and J. Yang, “Multi-Scale Context Aggregation for Robust Underwater Object Detection,” IEEE Trans. Image Process., vol. 30, pp. 3457–3469, 2021.
18. T. Wang, C. Ma, and B. Li, “YOLO-U: An Improved YOLO-Based Detection Network for Underwater Objects,” in Proc. Int. Conf. on Computer Vision and Graphics (ICCVG), 2022, pp. 101–110.
19. R. Zhao and M. Xu, “Vision-Based Underwater Object Detection Using Deep Neural Networks: A Review,” IEEE Sensors Journal, vol. 22, no. 7, pp. 6232–6245, 2022.
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21. M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” arXiv preprint arXiv:1701.07875, 2017.
22. S. Karras et al., “Training generative adversarial networks with limited data,” Proc. NeurIPS, vol. 33, pp. 12104–12114, 2020.
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24. OpenCV, “Open Source Computer Vision Library,” [Online]. Available: https://opencv.org
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2. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 779–788.
3. A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020.
4. L. Li, Y. Guo, and Y. Zhu, “Underwater Object Detection Using Deep Learning for Marine Applications,” in Proc. IEEE/OES China Ocean Acoustics (COA), Harbin, China, 2016, pp. 1–6.
5. Z. Islam, S. Zhang, and J. Li, “Underwater Image Enhancement Using Deep Learning and Generative Adversarial Networks,” IEEE Access, vol. 8, pp. 123362–123373, 2020.
6. Y. Liu, X. Fu, X. Ding, Y. Huang, and J. Paisley, “A Benchmark Dataset and Learning Pipeline for Underwater Image Enhancement,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2020, pp. 513–522.
7. W. Wang, Y. Wang, and H. Wang, “An Improved Deep Learning Model for Underwater Object Recognition Based on YOLOv5,” in Proc. Int. Conf. Intell. Comput. and Human-Computer Interaction (ICHCI), 2022, pp. 189–193.
8. M. P. Jahan, A. H. M. Razib, and M. F. Bari, “Real-Time Object Detection for Autonomous Underwater Vehicles Using Deep Learning,” in Proc. IEEE Reg. 10 Symp. (TENSYMP), 2021, pp. 1–6.
9. S. Anwar, C. Li, and F. Porikli, “Deep Underwater Image Enhancement,” arXiv preprint arXiv:1807.03528, 2018.
10. H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid Scene Parsing Network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 2881–2890.
11. C. Li, S. Anwar, and F. Porikli, “Underwater Scene Prior Inspired Deep Underwater Image and Video Enhancement,” Pattern Recognition, vol. 98, p. 107038, 2020.
12. M. Islam, C. Li, S. Anwar, and F. Porikli, “Fast Underwater Image Enhancement for Improved Visual Perception,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3227–3234, Apr. 2020.
13. Y. Jiang, X. Sun, and S. Liu, “Enhanced YOLOv5 for Real-Time Underwater Object Detection,” in Proc. Int. Conf. on Artificial Intelligence and Computer Engineering (ICAICE), 2021, pp. 123–128.
14. D. Han, Q. Li, and X. Zhang, “A Robust Underwater Object Detection System Using Deep Transfer Learning,” Sensors, vol. 21, no. 4, pp. 1–17, 2021.
15. L. Wang, Y. Fu, and Y. Liu, “Image Enhancement Techniques for Underwater Object Recognition: A Survey,” IEEE Access, vol. 9, pp. 123456–123474, 2021.
16. M. Liu, X. Wu, and H. Xu, “Domain Adaptation for Underwater Object Detection Using Style Transfer,” in Proc. IEEE Int. Conf. Image Process. (ICIP), 2020, pp. 3511–3515.
17. H. Fang, Z. Zhang, and J. Yang, “Multi-Scale Context Aggregation for Robust Underwater Object Detection,” IEEE Trans. Image Process., vol. 30, pp. 3457–3469, 2021.
18. T. Wang, C. Ma, and B. Li, “YOLO-U: An Improved YOLO-Based Detection Network for Underwater Objects,” in Proc. Int. Conf. on Computer Vision and Graphics (ICCVG), 2022, pp. 101–110.
19. R. Zhao and M. Xu, “Vision-Based Underwater Object Detection Using Deep Neural Networks: A Review,” IEEE Sensors Journal, vol. 22, no. 7, pp. 6232–6245, 2022.
20. J. Yoon, S. Kim, and S. Choi, “Temporal GANs conditioning on action sequences,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 6, pp. 1453–1465, Jun. 2020.
21. M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” arXiv preprint arXiv:1701.07875, 2017.
22. S. Karras et al., “Training generative adversarial networks with limited data,” Proc. NeurIPS, vol. 33, pp. 12104–12114, 2020.
23. Y. Luo, J. Ren, and M. Liu, “Face aging and rejuvenation by conditional multi-adversarial autoencoder with ranking loss,” IEEE Access, vol. 8, pp. 127638–127651, 2020.
24. OpenCV, “Open Source Computer Vision Library,” [Online]. Available: https://opencv.org
25. Streamlit, “Streamlit Documentation,” [Online]. Available: https://docs.streamlit.io/
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