Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation
The timely, automatic, and accurate detection of water-surface targets has received significant attention in intelligent vision-enabled maritime transportation systems. The reliable detection results are also beneficial for water quality monitoring in practical applications. However, the visual imag...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/9470895 |
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| _version_ | 1832566281323151360 |
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| author | Yongqi Guo Yuxu Lu Yu Guo Ryan Wen Liu Kwok Tai Chui |
| author_facet | Yongqi Guo Yuxu Lu Yu Guo Ryan Wen Liu Kwok Tai Chui |
| author_sort | Yongqi Guo |
| collection | DOAJ |
| description | The timely, automatic, and accurate detection of water-surface targets has received significant attention in intelligent vision-enabled maritime transportation systems. The reliable detection results are also beneficial for water quality monitoring in practical applications. However, the visual image quality is often inevitably degraded due to the poor weather conditions, potentially leading to unsatisfactory target detection results. The degraded images could be restored using state-of-the-art visibility enhancement methods. It is still difficult to generate high-quality detection performance due to the unavoidable loss of details in restored images. To alleviate these limitations, we first investigate the influences of visibility enhancement methods on detection results and then propose a neural network-empowered water-surface target detection framework. A data augmentation strategy, which synthetically simulates the degraded images under different weather conditions, is further presented to promote the generalization and feature representation abilities of our network. The proposed detection performance has the capacity of accurately detecting the water-surface targets under different adverse imaging conditions, e.g., haze, low-lightness, and rain. Experimental results on both synthetic and realistic scenarios have illustrated the effectiveness of the proposed framework in terms of detection accuracy and efficacy. |
| format | Article |
| id | doaj-art-694724abe7924002afc0c8706c9a544f |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-694724abe7924002afc0c8706c9a544f2025-02-03T01:04:32ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/94708959470895Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime TransportationYongqi Guo0Yuxu Lu1Yu Guo2Ryan Wen Liu3Kwok Tai Chui4Center of Teaching Supervision, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Science and Technology, The Open University of Hong Kong, Ho Man Tin, Hong KongThe timely, automatic, and accurate detection of water-surface targets has received significant attention in intelligent vision-enabled maritime transportation systems. The reliable detection results are also beneficial for water quality monitoring in practical applications. However, the visual image quality is often inevitably degraded due to the poor weather conditions, potentially leading to unsatisfactory target detection results. The degraded images could be restored using state-of-the-art visibility enhancement methods. It is still difficult to generate high-quality detection performance due to the unavoidable loss of details in restored images. To alleviate these limitations, we first investigate the influences of visibility enhancement methods on detection results and then propose a neural network-empowered water-surface target detection framework. A data augmentation strategy, which synthetically simulates the degraded images under different weather conditions, is further presented to promote the generalization and feature representation abilities of our network. The proposed detection performance has the capacity of accurately detecting the water-surface targets under different adverse imaging conditions, e.g., haze, low-lightness, and rain. Experimental results on both synthetic and realistic scenarios have illustrated the effectiveness of the proposed framework in terms of detection accuracy and efficacy.http://dx.doi.org/10.1155/2021/9470895 |
| spellingShingle | Yongqi Guo Yuxu Lu Yu Guo Ryan Wen Liu Kwok Tai Chui Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation Journal of Advanced Transportation |
| title | Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation |
| title_full | Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation |
| title_fullStr | Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation |
| title_full_unstemmed | Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation |
| title_short | Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation |
| title_sort | intelligent vision enabled detection of water surface targets for video surveillance in maritime transportation |
| url | http://dx.doi.org/10.1155/2021/9470895 |
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