Noise Robust Underwater Fishing Net Recognition Based on Range Gated Imaging
Underwater fishing net recognition plays an indispensable role in applications such as safe navigation of unmanned underwater vehicles, protection of marine ecology and marine ranching. However, the performance of underwater fishing net recognition usually degrades seriously due to noise interferenc...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10772403/ |
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| _version_ | 1841533384522727424 |
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| author | Zhensong Xu Xinwei Wang Liang Sun Bo Song Yue Zhang Pingshun Lei Jianan Chen Jun He Yan Zhou Yuliang Liu |
| author_facet | Zhensong Xu Xinwei Wang Liang Sun Bo Song Yue Zhang Pingshun Lei Jianan Chen Jun He Yan Zhou Yuliang Liu |
| author_sort | Zhensong Xu |
| collection | DOAJ |
| description | Underwater fishing net recognition plays an indispensable role in applications such as safe navigation of unmanned underwater vehicles, protection of marine ecology and marine ranching. However, the performance of underwater fishing net recognition usually degrades seriously due to noise interference in underwater environments. In this paper, we use range gated imaging as the detection device, and propose a semantic fishing net recognition network (SFNR-Net) for underwater fishing net recognition at long distance. The proposed SFNR-Net introduces an auxiliary semantic segmentation module (ASSM) to introduce extra semantic information and enhance feature representation under noisy conditions. Besides, to address the problem of unbalanced training data, we employ semantic regulated cycle-consistent generative adversarial network (CycleGAN) as a data augmentation approach. To improve the quality of generated data, we propose a semantic loss to regulate the training of CycleGAN. Comprehensive experiments on the test data show that SFNR-Net can effectively solve noise interference and achieve the best recognition accuracy of 96.28% compared with existing methods. Field experiments in underwater environments with different turbidity further validate the advantages of our method. |
| format | Article |
| id | doaj-art-012917459aa34fae9ab4b62e40a85840 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-012917459aa34fae9ab4b62e40a858402025-01-16T00:02:08ZengIEEEIEEE Access2169-35362024-01-011218549218551010.1109/ACCESS.2024.351033510772403Noise Robust Underwater Fishing Net Recognition Based on Range Gated ImagingZhensong Xu0Xinwei Wang1https://orcid.org/0009-0009-5724-9089Liang Sun2Bo Song3Yue Zhang4Pingshun Lei5Jianan Chen6Jun He7Yan Zhou8Yuliang Liu9https://orcid.org/0000-0003-1404-2239Optoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaOptoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaOptoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaOptoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaOptoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaOptoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaOptoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaOptoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaOptoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaOptoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaUnderwater fishing net recognition plays an indispensable role in applications such as safe navigation of unmanned underwater vehicles, protection of marine ecology and marine ranching. However, the performance of underwater fishing net recognition usually degrades seriously due to noise interference in underwater environments. In this paper, we use range gated imaging as the detection device, and propose a semantic fishing net recognition network (SFNR-Net) for underwater fishing net recognition at long distance. The proposed SFNR-Net introduces an auxiliary semantic segmentation module (ASSM) to introduce extra semantic information and enhance feature representation under noisy conditions. Besides, to address the problem of unbalanced training data, we employ semantic regulated cycle-consistent generative adversarial network (CycleGAN) as a data augmentation approach. To improve the quality of generated data, we propose a semantic loss to regulate the training of CycleGAN. Comprehensive experiments on the test data show that SFNR-Net can effectively solve noise interference and achieve the best recognition accuracy of 96.28% compared with existing methods. Field experiments in underwater environments with different turbidity further validate the advantages of our method.https://ieeexplore.ieee.org/document/10772403/Underwater image recognitionsemantic segmentationrange gated imagingdeep learninggenerative adversarial network |
| spellingShingle | Zhensong Xu Xinwei Wang Liang Sun Bo Song Yue Zhang Pingshun Lei Jianan Chen Jun He Yan Zhou Yuliang Liu Noise Robust Underwater Fishing Net Recognition Based on Range Gated Imaging IEEE Access Underwater image recognition semantic segmentation range gated imaging deep learning generative adversarial network |
| title | Noise Robust Underwater Fishing Net Recognition Based on Range Gated Imaging |
| title_full | Noise Robust Underwater Fishing Net Recognition Based on Range Gated Imaging |
| title_fullStr | Noise Robust Underwater Fishing Net Recognition Based on Range Gated Imaging |
| title_full_unstemmed | Noise Robust Underwater Fishing Net Recognition Based on Range Gated Imaging |
| title_short | Noise Robust Underwater Fishing Net Recognition Based on Range Gated Imaging |
| title_sort | noise robust underwater fishing net recognition based on range gated imaging |
| topic | Underwater image recognition semantic segmentation range gated imaging deep learning generative adversarial network |
| url | https://ieeexplore.ieee.org/document/10772403/ |
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