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

Abstract

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.