Deep Learning-Based Cascaded Light Source Detection for Link Alignment in Underwater Wireless Optical Communication
Obtaining the light source position from the image is an important solution for achieving link alignment in laser-based underwater wireless optical communication (UWOC) systems. However, in practical scenarios, the misalignment degree between the light source and camera is variable, and factors such...
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IEEE
2024-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10663260/ |
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| author | Bowen Jia Wenmin Ge Jingxuan Cheng Zihao Du Renming Wang Guangbin Song Yufan Zhang Chengye Cai Sitong Qin Jing Xu |
| author_facet | Bowen Jia Wenmin Ge Jingxuan Cheng Zihao Du Renming Wang Guangbin Song Yufan Zhang Chengye Cai Sitong Qin Jing Xu |
| author_sort | Bowen Jia |
| collection | DOAJ |
| description | Obtaining the light source position from the image is an important solution for achieving link alignment in laser-based underwater wireless optical communication (UWOC) systems. However, in practical scenarios, the misalignment degree between the light source and camera is variable, and factors such as ambient light may introduce disturbances, leading to significant variations in the appearance of light spots in images. Existing research primarily relies on simple features like brightness, color, or shape, which makes it difficult to accurately obtain position information from these non-ideal images. In this paper, deep neural networks (DNNs) with strong feature extraction capabilities are introduced to automatically learn the patterns of the light source from diverse images. A detection architecture cascading an object detector and a keypoint detector is adopted, achieving better comprehensive performance in terms of accuracy and speed. To train and evaluate the deep learning model, we construct the UWOC Light Source Detection Benchmark (ULDB) dataset. This dataset comprises 2200 images captured in a standard swimming pool, covering a misalignment range far beyond existing studies. On the ULDB test set, the proposed detection method achieves an average precision (AP) of 99.1% and an average positioning error of 4.66 pixels, while the traditional method may frequently extract false light spots. To the best of our knowledge, the ULDB dataset is the first image dataset specifically designed for the task of link alignment between UWOC terminals. |
| format | Article |
| id | doaj-art-b3dd0820b062418d8486291dec281140 |
| institution | OA Journals |
| issn | 1943-0655 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-b3dd0820b062418d8486291dec2811402025-08-20T02:38:36ZengIEEEIEEE Photonics Journal1943-06552024-01-0116511210.1109/JPHOT.2024.345311610663260Deep Learning-Based Cascaded Light Source Detection for Link Alignment in Underwater Wireless Optical CommunicationBowen Jia0https://orcid.org/0009-0003-0881-3727Wenmin Ge1Jingxuan Cheng2https://orcid.org/0009-0008-7177-064XZihao Du3Renming Wang4Guangbin Song5Yufan Zhang6https://orcid.org/0009-0004-4347-1272Chengye Cai7https://orcid.org/0000-0003-3672-728XSitong Qin8Jing Xu9https://orcid.org/0000-0001-6430-5063Hainan Institute of Zhejiang University, Sanya, ChinaOptical Communications Laboratory, Ocean College, Zhejiang University, Zhoushan, ChinaOptical Communications Laboratory, Ocean College, Zhejiang University, Zhoushan, ChinaOptical Communications Laboratory, Ocean College, Zhejiang University, Zhoushan, ChinaHainan Institute of Zhejiang University, Sanya, ChinaHainan Institute of Zhejiang University, Sanya, ChinaOptical Communications Laboratory, Ocean College, Zhejiang University, Zhoushan, ChinaOptical Communications Laboratory, Ocean College, Zhejiang University, Zhoushan, ChinaOptical Communications Laboratory, Ocean College, Zhejiang University, Zhoushan, ChinaHainan Institute of Zhejiang University, Sanya, ChinaObtaining the light source position from the image is an important solution for achieving link alignment in laser-based underwater wireless optical communication (UWOC) systems. However, in practical scenarios, the misalignment degree between the light source and camera is variable, and factors such as ambient light may introduce disturbances, leading to significant variations in the appearance of light spots in images. Existing research primarily relies on simple features like brightness, color, or shape, which makes it difficult to accurately obtain position information from these non-ideal images. In this paper, deep neural networks (DNNs) with strong feature extraction capabilities are introduced to automatically learn the patterns of the light source from diverse images. A detection architecture cascading an object detector and a keypoint detector is adopted, achieving better comprehensive performance in terms of accuracy and speed. To train and evaluate the deep learning model, we construct the UWOC Light Source Detection Benchmark (ULDB) dataset. This dataset comprises 2200 images captured in a standard swimming pool, covering a misalignment range far beyond existing studies. On the ULDB test set, the proposed detection method achieves an average precision (AP) of 99.1% and an average positioning error of 4.66 pixels, while the traditional method may frequently extract false light spots. To the best of our knowledge, the ULDB dataset is the first image dataset specifically designed for the task of link alignment between UWOC terminals.https://ieeexplore.ieee.org/document/10663260/Communication link alignmentdeep learningobject detectionkeypoint detectionunderwater wireless optical communication (UWOC) |
| spellingShingle | Bowen Jia Wenmin Ge Jingxuan Cheng Zihao Du Renming Wang Guangbin Song Yufan Zhang Chengye Cai Sitong Qin Jing Xu Deep Learning-Based Cascaded Light Source Detection for Link Alignment in Underwater Wireless Optical Communication IEEE Photonics Journal Communication link alignment deep learning object detection keypoint detection underwater wireless optical communication (UWOC) |
| title | Deep Learning-Based Cascaded Light Source Detection for Link Alignment in Underwater Wireless Optical Communication |
| title_full | Deep Learning-Based Cascaded Light Source Detection for Link Alignment in Underwater Wireless Optical Communication |
| title_fullStr | Deep Learning-Based Cascaded Light Source Detection for Link Alignment in Underwater Wireless Optical Communication |
| title_full_unstemmed | Deep Learning-Based Cascaded Light Source Detection for Link Alignment in Underwater Wireless Optical Communication |
| title_short | Deep Learning-Based Cascaded Light Source Detection for Link Alignment in Underwater Wireless Optical Communication |
| title_sort | deep learning based cascaded light source detection for link alignment in underwater wireless optical communication |
| topic | Communication link alignment deep learning object detection keypoint detection underwater wireless optical communication (UWOC) |
| url | https://ieeexplore.ieee.org/document/10663260/ |
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