A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity
With the rapid development of global maritime trade, high-precision ship heading estimation has become crucial for maritime traffic safety and intelligent shipping. To address the challenge of heading estimation from horizontal-view optical images, this study proposes a novel framework integrating D...
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/6/1085 |
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| author | Weihao Tao Yasong Luo Jijin Tong Qingtao Xia Jianjing Qu |
| author_facet | Weihao Tao Yasong Luo Jijin Tong Qingtao Xia Jianjing Qu |
| author_sort | Weihao Tao |
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| description | With the rapid development of global maritime trade, high-precision ship heading estimation has become crucial for maritime traffic safety and intelligent shipping. To address the challenge of heading estimation from horizontal-view optical images, this study proposes a novel framework integrating DeepLabV3+ image segmentation with contrastive-learning-optimized multi-scale similarity matching. First, a cascaded image preprocessing method is developed, incorporating linear transformation, bilateral filtering, and the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm to mitigate noise and haze interference and enhance image quality with improved target edge clarity. Subsequently, the DeepLabV3+ network is employed for the precise segmentation of ship targets, generating binarized contour maps for subsequent heading analysis. Based on actual ship dimensional parameters, 3D models are constructed and multi-angle rendered to establish a heading template library. The framework introduces the Multi-Scale Structural Similarity (MS-SSIM) algorithm enhanced by a triplet contrastive learning mechanism that dynamically optimizes feature weights across scales, thereby improving robustness against image degradation and partial occlusion. Experimental results demonstrate that under noise-free, noise-interfered, and mist-occluded conditions, the proposed method achieves mean heading estimation errors of 0.41°, 0.65°, and 0.88°, respectively, significantly outperforming the single-scale SSIM and fixed-weight MS-SSIM approaches. This verification confirms the method’s effectiveness and robustness, offering a novel technical solution for ship heading estimation in maritime surveillance and intelligent navigation systems. |
| format | Article |
| id | doaj-art-91d96816e41b45908744a7596e4d44ea |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-91d96816e41b45908744a7596e4d44ea2025-08-20T03:27:22ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-01136108510.3390/jmse13061085A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale SimilarityWeihao Tao0Yasong Luo1Jijin Tong2Qingtao Xia3Jianjing Qu4College of Weaponry Engineering, Naval University of Engineering, Wuhan 430030, ChinaCollege of Weaponry Engineering, Naval University of Engineering, Wuhan 430030, ChinaCollege of Weaponry Engineering, Naval University of Engineering, Wuhan 430030, ChinaCollege of Weaponry Engineering, Naval University of Engineering, Wuhan 430030, ChinaJiu Zhi Yang Infrared System Co., Ltd., Wuhan 430223, ChinaWith the rapid development of global maritime trade, high-precision ship heading estimation has become crucial for maritime traffic safety and intelligent shipping. To address the challenge of heading estimation from horizontal-view optical images, this study proposes a novel framework integrating DeepLabV3+ image segmentation with contrastive-learning-optimized multi-scale similarity matching. First, a cascaded image preprocessing method is developed, incorporating linear transformation, bilateral filtering, and the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm to mitigate noise and haze interference and enhance image quality with improved target edge clarity. Subsequently, the DeepLabV3+ network is employed for the precise segmentation of ship targets, generating binarized contour maps for subsequent heading analysis. Based on actual ship dimensional parameters, 3D models are constructed and multi-angle rendered to establish a heading template library. The framework introduces the Multi-Scale Structural Similarity (MS-SSIM) algorithm enhanced by a triplet contrastive learning mechanism that dynamically optimizes feature weights across scales, thereby improving robustness against image degradation and partial occlusion. Experimental results demonstrate that under noise-free, noise-interfered, and mist-occluded conditions, the proposed method achieves mean heading estimation errors of 0.41°, 0.65°, and 0.88°, respectively, significantly outperforming the single-scale SSIM and fixed-weight MS-SSIM approaches. This verification confirms the method’s effectiveness and robustness, offering a novel technical solution for ship heading estimation in maritime surveillance and intelligent navigation systems.https://www.mdpi.com/2077-1312/13/6/1085ship heading estimationmulti-scale structural similaritycontrastive learningDeepLabV3+image enhancement |
| spellingShingle | Weihao Tao Yasong Luo Jijin Tong Qingtao Xia Jianjing Qu A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity Journal of Marine Science and Engineering ship heading estimation multi-scale structural similarity contrastive learning DeepLabV3+ image enhancement |
| title | A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity |
| title_full | A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity |
| title_fullStr | A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity |
| title_full_unstemmed | A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity |
| title_short | A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity |
| title_sort | ship heading estimation method based on deeplabv3 and contrastive learning optimized multi scale similarity |
| topic | ship heading estimation multi-scale structural similarity contrastive learning DeepLabV3+ image enhancement |
| url | https://www.mdpi.com/2077-1312/13/6/1085 |
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