Improving Underwater Object Detection and Classification using Deep Learning for ROVs
DOI:
https://doi.org/10.52783/jns.v14.3001Keywords:
Underwater Object Detection, Computer Vision, Remotely Operated Vehicles (ROVs), Metal Object RecognitionAbstract
The identification and categorization of things, particularly metallic artefacts, is a substantial problem in underwater research for many reasons. This paper presents a thorough algorithmic framework for underwater metal object detection and classification using remotely operated vehicles (ROVs) and computer vision. The experimental design section describes the steps used to detect objects underwater with ROVs. The algorithm is subjected to several processes, including picture enhancement, object identification using YOLOv3, and object classification using Deep learning algorithm. Both the training and testing datasets provide a wide range of underwater images with different lighting, object sizes, and complexity of backgrounds. Analyses and Results detail the assessment of the combined algorithm's performance. We use the industry-standard metrics for object detection, such as F1 score, precision, recall, and Intersection over Union (IoU). When tested on a variety of metallic items, the programme consistently returns positive results. Further validation of the algorithm's ability in identifying and classifying specific items underwater is provided by a comparative examination of precision, recall, and F1 score across different classes.
Downloads
Metrics
References
A. Jesus, C. Zito, C. Tortorici, E. Roura, and G. De Masi, “Underwater object classification and detection: first results and open challenges,” OCEANS 2022-Chennai, pp. 1–6, 2022.
K. Katija, “Autonomous agents for observing marine life,” Science Robotics, vol. 8, no. 80, p. eadi6428, 2023.
D. Akkaynak and T. Treibitz, “Sea-thru: A method for removing water from underwater images,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1682–1691, 2019.
J. Wen, J. Cui, Z. Zhao, R. Yan, Z. Gao, L. Dou, and B. M. Chen, “Syreanet: A physically guided underwater image enhancement framework integrating synthetic and real images,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 5177– 5183, 2023.
L. Jiang, Y. Wang, Q. Jia, S. Xu, Y. Liu, X. Fan, H. Li, R. Liu, X. Xue, and R. Wang, “Underwater species detection using channel sharpening attention,” in Proceedings of the 29th ACM International Conference on Multimedia, pp. 4259–4267, 2021.
Saravanakumar, S. (2020). Certain analysis of authentic user behavioral and opinion pattern mining using classification techniques. Solid State Technology, 63(6), 9220-9234.
F. Zocco, C.-I. Huang, H.-C. Wang, M. O. Khyam, and M. Van, “Towards more efficient efficientdets and low-light real-time marine debris detection,” ArXiv, vol. abs/2203.07155, 2022.
S. Sun, W. Ren, T. Wang, and X. Cao, “Rethinking image restoration for object detection,” Advances in Neural Information Processing Systems, vol. 35, pp. 4461–4474, 2022.
Kumaresan, T., Saravanakumar, S., & Balamurugan, R. (2019). Visual and textual features based email spam classification using S-Cuckoo search and hybrid kernel support vector machine. Cluster Computing, 22(Suppl 1), 33-46.
W. Liu, G. Ren, R. Yu, S. Guo, J. Zhu, and L. Zhang, “Imageadaptive yolo for object detection in adverse weather conditions,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 1792–1800, 2022.
Y.-W. Chen and S.-C. Pei, “Domain adaptation for underwater image enhancement via content and style separation,” arXiv preprint arXiv:2202.08537, 2022.
P. Drews, E. Nascimento, F. Moraes, S. Botelho, and M. Campos, “Transmission estimation in underwater single images,” in Proceedings of the IEEE international conference on computer vision workshops, pp. 825–830, 2013.
D. Berman, T. Treibitz, and S. Avidan, “Diving into haze-lines: Color restoration of underwater images,” in Proc. British Machine Vision Conference (BMVC), vol. 1, 2017.
R. Wang, Y. Zhang, and J. Zhang, “An efficient swin transformerbased method for underwater image enhancement,” Multimedia Tools and Applications, vol. 82, no. 12, pp. 18691–18708, 2023.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision, pp. 10012–10022, 2021.
S. Huang, K. Wang, H. Liu, J. Chen, and Y. Li, “Contrastive semisupervised learning for underwater image restoration via reliable bank,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18145–18155, 2023.
Saravanan, T., & Saravanakumar, S. (2022). Enhancing investigations in data migration and security using sequence cover cat and cover particle swarm optimization in the fog paradigm. International Journal of Intelligent Networks, 3, 204-212.
S. Jamieson, J. P. How, and Y. Girdhar, “Deepseecolor: Realtime adaptive color correction for autonomous underwater vehicles via deep learning methods,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 3095–3101, 2023. [16] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards realtime object detection with region proposal networks,” Advances in neural information processing systems, vol. 28, 2015.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779–788, 2016.
Saravanakumar, S., & Thangaraj, P. (2019). A computer aided diagnosis system for identifying Alzheimer’s from MRI scan using improved Adaboost. Journal of medical systems, 43(3), 76.
M. A. Syariz, C.-H. Lin, M. V. Nguyen, L. M. Jaelani, and A. C. Blanco, “Waternet: A convolutional neural network for chlorophylla concentration retrieval,” Remote Sensing, vol. 12, no. 12, p. 1966, 2020
Saravanakumar, S., & Saravanan, T. (2023). Secure personal authentication in fog devices via multimodal rank‐level fusion. Concurrency and Computation: Practice and Experience, 35(10), e7673.
Thangavel, S., & Selvaraj, S. (2023). Machine Learning Model and Cuckoo Search in a modular system to identify Alzheimer’s disease from MRI scan images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(5), 1753-1761.
Saravanan, T., Saravanakumar, S., Rathinam, G. O. P. A. L., Narayanan, M., Poongothai, T., Patra, P. S. K., & Sengan, S. U. D. H. A. K. A. R. (2022). Malicious attack alleviation using improved time-based dimensional traffic pattern generation in uwsn. Journal of Theoretical and Applied Information Technology, 100(3), 682-689.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
