A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity
Shranjeno v:
| izdano v: | Journal of Marine Science and Engineering vol. 13, no. 6 (2025), p. 1085-1112 |
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| Glavni avtor: | |
| Drugi avtorji: | , , , |
| Izdano: |
MDPI AG
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| Teme: | |
| Online dostop: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 100 | 1 | |a Weihao, Tao |u College of Weaponry Engineering, Naval University of Engineering, Wuhan 430030, China; m23182605@nue.edu.cn (W.T.); tongjj7802@sina.com (J.T.); xiaqing777@163.com (Q.X.) | |
| 245 | 1 | |a A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Navigation systems | ||
| 653 | |a Similarity | ||
| 653 | |a Accuracy | ||
| 653 | |a Shipping | ||
| 653 | |a Adaptability | ||
| 653 | |a Algorithms | ||
| 653 | |a Optimization | ||
| 653 | |a Image degradation | ||
| 653 | |a Image processing | ||
| 653 | |a Retinex (algorithm) | ||
| 653 | |a Linear transformations | ||
| 653 | |a Occlusion | ||
| 653 | |a Remote sensing | ||
| 653 | |a Learning | ||
| 653 | |a Image segmentation | ||
| 653 | |a Sensors | ||
| 653 | |a Three dimensional models | ||
| 653 | |a Traffic accidents & safety | ||
| 653 | |a Methods | ||
| 653 | |a Image quality | ||
| 653 | |a Robustness (mathematics) | ||
| 653 | |a Noise | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Luo Yasong |u College of Weaponry Engineering, Naval University of Engineering, Wuhan 430030, China; m23182605@nue.edu.cn (W.T.); tongjj7802@sina.com (J.T.); xiaqing777@163.com (Q.X.) | |
| 700 | 1 | |a Tong Jijin |u College of Weaponry Engineering, Naval University of Engineering, Wuhan 430030, China; m23182605@nue.edu.cn (W.T.); tongjj7802@sina.com (J.T.); xiaqing777@163.com (Q.X.) | |
| 700 | 1 | |a Xia Qingtao |u College of Weaponry Engineering, Naval University of Engineering, Wuhan 430030, China; m23182605@nue.edu.cn (W.T.); tongjj7802@sina.com (J.T.); xiaqing777@163.com (Q.X.) | |
| 700 | 1 | |a Qu Jianjing |u Jiu Zhi Yang Infrared System Co., Ltd., Wuhan 430223, China; qjj13971446541@163.com | |
| 773 | 0 | |t Journal of Marine Science and Engineering |g vol. 13, no. 6 (2025), p. 1085-1112 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223914969/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223914969/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223914969/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |