Mask R-CNN-Based Detection of Ship Coating Corrosion

船舶长期暴露在高盐高湿的海洋环境中，腐蚀问题严重影响其结构安全与运营效率。传统人工检测存在效率低、精度不足的局限性，而传统目标检测算法并未针对船舶腐蚀问题进行有针对性的优化。因此，本文提出了一种基于Mask R-CNN的智能船舶腐蚀检测方法。本文通过多源数据整合与模型优化，实现腐蚀区域的精准识别，研究汇集各开源数据，构建包含135张高分辨率图像的标准数据集，并在预处理后利用Labelme进行像素级精细的多边形标注。在模型上，本研究选择结合特征金字塔网络(FPN)与改进型ROIAlign模块，并采用动态学习率策略优化训练，通过迁移学习策略解决了船舶腐蚀检测数据集合数量不足的问题。最后的实验结果表明，Mask R-CNN结合FPN结构与128 × ROIAlign模块的模型在多尺度检测方面表现优异，平均精度(AP)达到0.414，较基准配置(Faster R-CNN结合C4结构与64 × ROIAlign模块)提升14.0%；小目标检测精度(APS)提高11.4%；边界定位精度(AP75)提升15.6%。本研究验证了深度学习在船舶腐蚀检测中的有效性，并提出轻量化部署方案，为工程应用提供技术支撑。Ships are chronically exposed to high-salinity and high-humidity marine environments, where corrosion severely compromises structural safety and operational efficiency. Traditional manual inspection suffers from inefficiency and limited accuracy, while conventional object detection algorithms lack targeted optimization for ship corrosion issues. To address these challenges, this paper proposes an intelligent ship corrosion detection method based on Mask R-CNN. Through multi-source data integration and model optimization, our approach achieves precise identification of corrosion areas. We aggregated open-source datasets to construct a standardized dataset comprising 135 high-resolution images, followed by pixel-level polygonal annotations using Labelme after preprocessing. For the model architecture, we integrated a Feature Pyramid Network (FPN) with an enhanced ROIAlign module and adopted a dynamic learning rate strategy to optimize training. A transfer learning strategy was employed to mitigate the limited dataset size for ship corrosion detection. Experimental results demonstrate that the Mask R-CNN model combined with FPN and a 128 × ROIAlign module excels in multi-scale detection, achieving a mean Average Precision (AP) of 0.414—a 14.0% improvement over the baseline configuration (Faster R-CNN with C4 backbone and 64×ROIAlign). Notably, small-target detection precision (APS) increased by 11.4%, and boundary localization accuracy (AP75) improved by 15.6%. This study validates the effectiveness of deep learning in ship corrosion detection and proposes a lightweight deployment solution, offering technical support for engineering applications.