A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity

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Detalles Bibliográficos
Publicado en:	Journal of Marine Science and Engineering vol. 13, no. 6 (2025), p. 1085-1112
Autor principal:	Weihao, Tao
Otros Autores:	Luo Yasong, Tong Jijin, Xia Qingtao, Qu Jianjing
Publicado:	MDPI AG
Materias:	Navigation systems Similarity Accuracy Shipping Adaptability Algorithms Optimization Image degradation Image processing Retinex (algorithm) Linear transformations Occlusion Remote sensing Learning Image segmentation Sensors Three dimensional models Traffic accidents & safety Methods Image quality Robustness (mathematics) Noise Environmental
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	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.
ISSN:	2077-1312
DOI:	10.3390/jmse13061085
Fuente:	Engineering Database