Hybrid Regularized Variational Minimization Method to Promote Visual Perception for Intelligent Surface Vehicles Under Hazy Weather Condition
Tallennettuna:
| Julkaisussa: | Journal of Marine Science and Engineering vol. 13, no. 10 (2025), p. 1991-2011 |
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| Päätekijä: | |
| Muut tekijät: | , , , |
| Julkaistu: |
MDPI AG
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| Aiheet: | |
| Linkit: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tagit: |
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| 001 | 3265915948 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2077-1312 | ||
| 024 | 7 | |a 10.3390/jmse13101991 |2 doi | |
| 035 | |a 3265915948 | ||
| 045 | 2 | |b d20251001 |b d20251031 | |
| 084 | |a 231479 |2 nlm | ||
| 100 | 1 | |a Li Peizheng |u School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China; lipeizheng@mail.nwpu.edu.cn | |
| 245 | 1 | |a Hybrid Regularized Variational Minimization Method to Promote Visual Perception for Intelligent Surface Vehicles Under Hazy Weather Condition | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Intelligent surface vehicles, including unmanned surface vehicles (USVs) and autonomous surface vehicles (ASVs), have gained significant attention from both academic and industrial communities. However, shipboard maritime images captured under hazy weather conditions inevitably suffer from a blurred, distorted appearance. Low-quality maritime images can lead to negative effects on high-level computer vision tasks, such as object detection, recognition and tracking, etc. To avoid the negative influence of low-quality maritime images, it is necessary to develop a visual perception enhancement method for intelligent surface vehicles. To generate satisfactory haze-free maritime images, we propose development of a novel transmission map estimation and refinement framework. In this work, the coarse transmission map is obtained by the weighted fusion of transmission maps generated by dark channel prior (DCP)- and luminance-based estimation methods. To refine the transmission map, we take the segmented smooth feature of the transmission map into account. A joint variational framework with total generalized variation (TGV) and relative total variation (RTV) regularizers is accordingly proposed. The joint variational framework is effectively solved by an alternating-direction numerical algorithm, which decomposes the original nonconvex nonsmooth optimization problem into several subproblems. Each subproblem could be efficiently and easily handled using the existing optimization algorithm. Finally, comprehensive experiments are conducted on synthetic and realistic maritime images. The imaging results have illustrated that our method can outperform or achieve comparable results with other competing dehazing methods. The promoted visual perception is beneficial to improve navigation safety for intelligent surface vehicles under hazy weather conditions. | |
| 653 | |a Visual perception | ||
| 653 | |a Deep learning | ||
| 653 | |a Algorithms | ||
| 653 | |a Surface vehicles | ||
| 653 | |a Weather | ||
| 653 | |a Autonomous surface vehicles | ||
| 653 | |a Unmanned vehicles | ||
| 653 | |a Computer vision | ||
| 653 | |a Maps | ||
| 653 | |a Numerical analysis | ||
| 653 | |a Neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Navigational safety | ||
| 653 | |a Regularization methods | ||
| 653 | |a Image quality | ||
| 653 | |a Perception | ||
| 653 | |a Object recognition | ||
| 653 | |a Navigation safety | ||
| 653 | |a Vehicles | ||
| 653 | |a Parameter estimation | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Qiao Dayong |u School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China; lipeizheng@mail.nwpu.edu.cn | |
| 700 | 1 | |a Luo Caofei |u China E-Tech (Ningbo) Maritime Electronics Research Institute Co., Ltd., Ningbo 315040, China; 12331136@zju.edu.cn (C.L.); wandesong2020@163.com (D.W.); gli.lee@foxmail.com (G.L.) | |
| 700 | 1 | |a Wan Desong |u China E-Tech (Ningbo) Maritime Electronics Research Institute Co., Ltd., Ningbo 315040, China; 12331136@zju.edu.cn (C.L.); wandesong2020@163.com (D.W.); gli.lee@foxmail.com (G.L.) | |
| 700 | 1 | |a Li, Guilian |u China E-Tech (Ningbo) Maritime Electronics Research Institute Co., Ltd., Ningbo 315040, China; 12331136@zju.edu.cn (C.L.); wandesong2020@163.com (D.W.); gli.lee@foxmail.com (G.L.) | |
| 773 | 0 | |t Journal of Marine Science and Engineering |g vol. 13, no. 10 (2025), p. 1991-2011 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3265915948/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3265915948/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3265915948/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |