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1- Faculty of Electrical and Robotics Engineering, Shahrood University of Technology, Semnan
2- Department of Electrical Engineering - Control, Faculty of Engineering, Imam Khomeini International University, Qazvin
2- Department of Electrical Engineering - Control, Faculty of Engineering, Imam Khomeini International University, Qazvin
Abstract: (420 Views)
Underwater images play a vital role in marine environmental research. Accurate analysis of these images is essential for managing marine ecosystems, preserving biodiversity, and monitoring environmental changes. However, various factors such as poor lighting, quality degradation due to light absorption and scattering, noise from suspended particles, and color distortions pose significant challenges in processing underwater images. These issues reduce the effectiveness of traditional image processing methods. Deep learning, as a powerful approach for automatically extracting complex features, can help mitigate or overcome these challenges. In this study, an intelligent deep learning method based on Capsule Neural Networks (CapsNets) is proposed for underwater image classification. By preserving spatial relationships among features and reducing reliance on pooling operations, CapsNets offer a better understanding of complex patterns. These characteristics enable effective handling of underwater image processing challenges. The proposed model, utilizing an advanced architecture, achieved higher classification accuracy compared to conventional methods and demonstrated robust performance under varying lighting conditions and image qualities. Experimental results showed that the proposed model outperformed existing approaches, achieving an overall accuracy of 96.75%. Moreover, the model exhibited stable performance across different underwater image classes, with accuracy ranging from 95.5% (lowest) to 98% (highest). Additionally, the average accuracy, sensitivity, and F1-score of the model were calculated as 96.75%, 96.75%, and 96.73%, respectively, indicating the model’s robustness in underwater image classification. Based on these findings, the proposed model demonstrates strong potential for a wide range of applications, including marine habitat monitoring, underwater exploration, conservation of rare species, and tracking environmental changes.
Keywords: Underwater Images, Capsule Neural Network, Image Classification, Underwater Image Processing, Deep Learning
Type of Study: Research Paper |
Subject:
Environmental Study
Received: 2025/03/8 | Accepted: 2025/10/6
Received: 2025/03/8 | Accepted: 2025/10/6
فایل صوتی چکیده مقاله [MP3 3476 KB] (10 Download)
References
1. S. Mittal, S. Srivastava, and J. P. Jayanth, "A Survey of Deep Learning Techniques for Underwater Image Classification," IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15, 2022.
https://doi.org/10.1109/TNNLS.2022.3143887 [DOI:10.1109/tnnls.2022.3143887.] [PMID]
2. T. T. Chungath, A. M. Nambiar, and A. Mittal, "Transfer learning and few-shot learning based deep neural network models for underwater sonar image classification with a few samples," IEEE Journal of Oceanic Engineering, vol. 49, no. 1, pp. 294-310, 2023.
https://doi.org/10.1109/JOE.2022.3221127 [DOI:10.1109/JOE.2022.3221127.]
3. M. S. Mohammed, H. A. Khater, Y. F. Hassan, and A. Elsayed, "Proposed approach for automatic underwater object classification," ICIC International, vol. 12, no. 12, pp. 1205-1212, 2018. https://doi.org/ 10.24507/icicel.12.12.1205. [DOI:10.24507/icicel.12.12.1205.]
4. M. Goyal, T. Knackstedt, S. Yan, and S. Hassanpour, "Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities," Computers in biology and medicine, vol. 127, p. 104065, 2020.
https://doi.org/10.1016/j.compbiomed.2020.104065 [DOI:10.1016/j.compbiomed.2020.104065.] [PMID] []
5. X. Du‐Harpur, F. Watt, N. Luscombe, and M. Lynch, "What is AI? Applications of artificial intelligence to dermatology," British Journal of Dermatology, vol. 183, no. 3, pp. 423-430, 2020.
https://doi.org/10.1111/bjd.18880 [DOI:10.1111/bjd.18880.] [PMID] []
6. A. Saleh, M. Sheaves, and M. Rahimi Azghadi, "Computer vision and deep learning for fish classification in underwater habitats: A survey," Fish and Fisheries, vol. 23, no. 4, pp. 977-999, 2022.
https://doi.org/10.1111/faf.12666 [DOI:10.1111/faf.12666.]
7. F. Han, J. Yao, H. Zhu, and C. Wang, "Underwater image processing and object detection based on deep CNN method," Journal of Sensors, vol. 2020, no. 1, p. 6707328, 2020.
https://doi.org/10.1155/2020/6707328 [DOI:10.1155/2020/6707328.]
8. J. Bharadiya, "Convolutional neural networks for image classification," International Journal of Innovative Science and Research Technology, vol. 8, no. 5, pp. 673-677, 2023.
9. M. Tripathi, "Analysis of convolutional neural network based image classification techniques," Journal of Innovative Image Processing (JIIP), vol. 3, no. 02, pp. 100-117, 2021.
https://doi.org/10.36548/jiip.2021.2.003 [DOI:10.36548/jiip.2021.2.003.]
10. M. Aridoss, C. Dhasarathan, A. Dumka, and J. Loganathan, "DUICM deep underwater image classification mobdel using convolutional neural networks," International Journal of Grid and High Performance Computing (IJGHPC), vol. 12, no. 3, pp. 88-100, 2020. [DOI:10.4018/IJGHPC.2020070106]
11. A. Mahmood, M. Bennamoun, S. An, F. Sohel, and F. Boussaid, "ResFeats: Residual network based features for underwater image classification," Image and Vision Computing, vol. 93, p. 103811, 2020.
https://doi.org/10.1016/j.imavis.2019.09.002 [DOI:10.1016/j.imavis.2019.09.002.]
12. M. Yang, H. Wang, K. Hu, G. Yin, and Z. Wei, "IA-Net: An inception-attention-module-based network for classifying underwater images from others," IEEE Journal of Oceanic Engineering, vol. 47, no. 3, pp. 704-717, 2022.
https://doi.org/10.1109/JOE.2021.3126090 [DOI:10.1109/JOE.2021.3126090.]
13. J. Yang, M. Cai, X. Yang, and Z. Zhou, "Underwater image classification algorithm based on convolutional neural network and optimized extreme learning machine," Journal of Marine Science and Engineering, vol. 10, no. 12, p. 1841, 2022.
https://doi.org/10.3390/jmse10121841 [DOI:10.3390/jmse10121841.]
14. G. Li et al., "MCANet: Multi-channel attention network with multi-color space encoder for underwater image classification," Computers and Electrical Engineering, vol. 108, p. 108724, 2023.
https://doi.org/10.1016/j.compeleceng.2023.108724 [DOI:10.1016/j.compeleceng.2023.108724.]
15. S. Xiao, X. Shen, Z. Zhang, J. Wen, M. Xi, and J. Yang, "Underwater image classification based on image enhancement and information quality evaluation," Displays, vol. 82, p. 102635, 2024.
https://doi.org/10.1016/j.displa.2023.102635 [DOI:10.1016/j.displa.2023.102635.]
16. S. Jamandlamudi, D. P. Isravel, and J. P. M. Dhas, "CNN Based Model for Underwater Image Classification and Enhancement," in 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), 2024: IEEE, pp. 1-8.
https://doi.org/10.1109/AMATHE61652.2024.10582240 [DOI:10.1109/AMATHE61652.2024.10582240.]
17. M. Khodadadzadeh, X. Ding, P. Chaurasia, and D. Coyle, "A hybrid capsule network for hyperspectral image classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 11824-11839, 2021.
https://doi.org/10.1109/JSTARS.2021.3126427 [DOI:10.1109/JSTARS.2021.3126427.]
18. Z. Sun, G. Zhao, R. Scherer, W. Wei, and M. Woźniak, "Overview of capsule neural networks," Journal of Internet Technology, vol. 23, no. 1, pp. 33-44, 2022. [DOI:10.53106/160792642022012301004]
19. M. Abdullah-Al-Wadud, Md. Kabir, M. Akber Dewan, and O. Chae, "A Dynamic Histogram Equalization for Image Contrast Enhancement," IEEE Transactions on Consumer Electronics, vol. 53, no. 2, pp. 593-600, 2007.
https://doi.org/10.1109/TCE.2007.381734 [DOI:10.1109/tce.2007.381734.]
20. V. T, "COMPARATIVE STUDY OF CAPSULE NEURAL NETWORK IN VARIOUS APPLICATIONS," Journal of Artificial Intelligence and Capsule Networks, vol. 01, no. 01, pp. 19-27, Sep. 2019.
https://doi.org/10.36548/jaicn.2019.1.003 [DOI:10.36548/jaicn.2019.1.003.]
21. P. Rodríguez, M. A. Bautista, J. Gonzàlez, and S. Escalera, "Beyond one-hot encoding: Lower dimensional target embedding," Image and Vision Computing, vol. 75, pp. 21-31, Jul. 2018.
https://doi.org/10.1016/j.imavis.2018.04.004 [DOI:10.1016/j.imavis.2018.04.004.]
22. A. Ali-Gombe and E. Elyan, "MFC-GAN: Class-imbalanced dataset classification using multiple fake class generative adversarial network," Neurocomputing, vol. 361, pp. 212-221, 2019.
https://doi.org/10.1016/j.neucom.2019.06.043 [DOI:10.1016/j.neucom.2019.06.043.]
23. N. Bigdeli, H. Jabbari, and M. Shojaei, "An intelligent method for crack classification in concrete structures based on deep neural networks, "Amirkabir Journal of Civil Engineering, vol. 53, no. 8, pp. 3201-3220. Oct. 2021. [DOI:10.22060/ceej.2020.17738.6660.]
24. H. Jabbari and Nooshin Bigdeli, "New conditional generative adversarial capsule network for imbalanced classification of human sperm head images," Neural Computing and Applications, vol. 35, no. 27, pp. 19919-19934, Jul. 2023.
https://doi.org/10.1007/s00521-023-08742-3 [DOI:10.1007/s00521-023-08742-3.]
25. H. Jabbari and N. Bigdeli, "A new hierarchical algorithm based on CapsGAN for imbalanced image classification," IET Image Processing, vol. 18, no. 1, pp. 194-210, Oct. 2023.
https://doi.org/10.1049/ipr2.12942 [DOI:10.1049/ipr2.12942.]
26. H. Jabbari and N. Bigdeli, "Design and Evaluation of a New Capsule Neural Network (CapsNet) for Imbalanced Images Classification," Journal of Machine Vision and Image Processing, vol. 9, no. 1, pp. 1-15, Apr. 2022.
27. H. Jabbari and N. Bigdeli, "A New Capsule Generative Adversarial Network for Imbalanced Classification of Human Sperm Images," Journal of Modeling in Engineering, vol. 21, no. 73, pp. 279-294. June. 2023. [DOI:10.22075/JME.2023.28349.2333.]
28. H. Jabbari, H. Hooshmand, and N. Bigdeli, "A Novel Intelligent Method Based on Capsule Networks for Maritime Ship," Classification. Journal of Machine Vision and Image Processing, Accepted Manuscript, Available Online from 11 August 2025.
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