A Hybrid Weiner Filter with MR-CNN for Object Detection in Underwater Image Processing
A Hybrid Weiner Filter with MR-CNN for Object Detection in Underwater Image Processing |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-1 |
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| Year of Publication : 2024 | ||
| Author : Hemalatha, Saravanan |
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| DOI : 10.14445/22315381/IJETT-V72I1P101 | ||
How to Cite?
Hemalatha, Saravanan, "A Hybrid Weiner Filter with MR-CNN for Object Detection in Underwater Image Processing," International Journal of Engineering Trends and Technology, vol. 72, no. 1, pp. 1-10, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I1P101
Abstract
Underwater image processing poses unique challenges due to the presence of various degradation factors such as color attenuation, backscatter, and noise. Object detection in underwater images is particularly challenging as these factors can affect the visibility and clarity of objects. In this study, we present a unique approach that combines the Weiner filter with a Multi-Resolution Convolutional Neural Network (MR-CNN) for a method for detecting objects underwater image processing to enhance object-detecting ability, and we integrate the Weiner filter with an MR-CNN. The MR-CNN utilizes multiple resolutions to capture and analyze different levels of information in the image. This multi-resolution approach allows for better extraction of features at various scales, enabling the network to detect accurately. The combination of the Weiner filter and the MR-CNN significantly improves the object detection accuracy in underwater images compared to traditional methods. The results highlight the potential for practical applications in underwater research, exploration, and surveillance domains
Keywords
Underwater image processing, Weiner filter, Multi-Resolution Convolutional Neural Network, Object detection, Surveillance domains.
References
[1] Lintao Peng, Chunli Zhu, and Liheng Bian, “U-Shape Transformer for Underwater Image Enhancement,” IEEE Transactions on Image Processing, vol. 32, pp. 3066-3079, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Jian Zhang et al., “An Improved YOLOv5-Based Underwater Object-Detection Framework,” Sensors, vol. 23, no. 7, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Shubo Xu et al., “A Systematic Review and Analysis of Deep Learning-based Underwater Object Detection,” Neurocomputing, vol. 527, pp. 204-232, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Boyang Sun et al., “UMGAN: Underwater Image Enhancement Network for Unpaired Image-to-Image Translation,” Journal of Marine Science and Engineering, vol. 11, no. 2, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Prem Kumari Verma, Nagendra Pratap Singh, and Divakar Yadav, “Image Enhancement: A Review,” Ambient Communications and Computer Systems, vol. 1097, pp. 347-355, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Yang Wang et al., “A Deep CNN Method for Underwater Image Enhancement,” IEEE International Conference on Image Processing (ICIP), Beijing, China, pp. 1382-1386, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Xiaobo Zhang, “Image Denoising Using Local Wiener Filter and its Method Noise,” Optik, vol. 127, no. 17, pp. 6821-6828, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Fei Wu et al., “Adaptive Wiener Filter and Natural Noise to Eliminate Adversarial Perturbation,” Electronics, vol. 9, no. 10, pp. 1-14, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Xin Sun, Lipeng Liu, and Junyu Dong, “Underwater Image Enhancement with Encoding-Decoding Deep CNN Networks,” IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), San Francisco, pp. 1-6, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Meicheng Zheng, and Weilin Luo, “Underwater Image Enhancement Using Improved CNN Based Defogging,” Electronics, vol. 11, no. 1, pp. 1-21, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Lanyong Zhang, Chengyu Li, and Hongfang Sun, “Object Detection/Tracking Toward Underwater Photographs by Remotely Operated Vehicles (ROVs),” Future Generation Computer Systems, vol. 126, pp. 163-168, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Radhwan Adnan Dakhil, and Ali Retha Hasoon Khayeat, “Review on Deep Learning Technique for Underwater Object Detection,” arXiv, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ganesh Babu Loganathan et al., “To Develop Multi-Object Detection and Recognition Using Improved GP-FRCNN Method,” 8 th International Conference on Smart Structures and Systems (ICSSS), Chennai, India, pp. 1-7. 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Keshetti Sreekala et al., “Deep Convolutional Neural Network with Kalman Filter Based Objected Tracking and Detection in Underwater Communications,” Wireless Networks, pp. 1-18, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Maofa Wang et al., “Study on Underwater Target Tracking Technology Based on an LSTM–Kalman Filtering Method,” Applied Sciences, vol. 12, no. 10, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Suja Cherukullapurath Mana, and T. Sasipraba, “An Intelligent Deep Learning Enabled Marine Fish Species Detection and Classification Model,” International Journal on Artificial Intelligence Tools, vol. 31, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Joaquin Gabaldon et al., “Computer-Vision Object Tracking for Monitoring Bottlenose Dolphin Habitat Use and Kinematics,” Plos One, vol. 17, no. 2, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Atif Naseer et al., “A Novel Detection Refinement Technique for Accurate Identification of Nephrops Norvegicus Burrows in Underwater Imagery,” Sensors, vol. 22, no. 12, pp. 1-22, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Rudrika Kalsotra, and Sakshi Arora, “Background Subtraction for Moving Object Detection: Explorations of Recent Developments and Challenges,” The Visual Computer, vol. 38, no. 12, pp. 4151-4178, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Ajisha Mathias, Samiappan Dhanalakshmi, and R. Kumar, “Occlusion Aware Underwater Object Tracking Using Hybrid Adaptive Deep SORT-YOLOv3 Approach,” Multimedia Tools and Applications, vol. 81, no. 30, pp. 44109-4412, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Jiayi Fan et al., “Improvement of Object Detection Based on Faster R-CNN and YOLO,” 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), Jeju, Korea (South), pp. 1-4, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Yu-Hsien Lin, Chao-Ming Yu, and Chia-Yu Wu, “Towards the Design and Implementation of an Image-Based Navigation System of an Autonomous Underwater Vehicle Combining a Color Recognition Technique and a Fuzzy Logic Controller,” Sensors, vol. 21, no. 12, pp. 1-23, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Daoliang Li et al., “Automatic Counting Methods in Aquaculture: A Review,” Journal of the World Aquaculture Society, vol. 52, no. 2, pp. 269-283, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Xianghui Li et al., “Intelligent Detection of Underwater Fish Speed Characteristics Based on Deep Learning,” 5 th Asian Conference on Artificial Intelligence Technology (ACAIT), Haikou, China, pp. 182-189, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Panpan Lu, Junyu Dong, and Xin Sun “Simple Online and Real-Time Underwater Multiple Object Tracking,” Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), Xi'an, China, vol. 11720, pp. 107-111, 2021.
[CrossRef] [Google Scholar] [Publisher Link]