论文标题
使用多阶段深度学习对海洋血管的表面缺陷检测和评估
Surface Defect Detection and Evaluation for Marine Vessels using Multi-Stage Deep Learning
论文作者
论文摘要
检测和评估表面涂层缺陷对于海洋血管维持很重要。目前，评估是由合格的检查员使用国际标准及其自身经验手动进行的。由于容器类型，油漆表面，涂料，照明条件，天气状况，油漆颜色，船只区域以及服务时间的差异很高，因此使工艺自动化非常具有挑战性。我们提出了一条新型的基于深度学习的管道，以检测和评估正常照片上容器表面上腐蚀，结垢和分层的百分比。我们提出了一个多阶段图像处理框架，包括船舶部分进行分割，缺陷分段和缺陷分类，以自动识别不同类型的缺陷并测量船体表面上的覆盖率百分比。实验结果表明，我们提出的管道可以客观地进行类似的评估作为合格的检查员。
Detecting and evaluating surface coating defects is important for marine vessel maintenance. Currently, the assessment is carried out manually by qualified inspectors using international standards and their own experience. Automating the processes is highly challenging because of the high level of variation in vessel type, paint surface, coatings, lighting condition, weather condition, paint colors, areas of the vessel, and time in service. We present a novel deep learning-based pipeline to detect and evaluate the percentage of corrosion, fouling, and delamination on the vessel surface from normal photographs. We propose a multi-stage image processing framework, including ship section segmentation, defect segmentation, and defect classification, to automatically recognize different types of defects and measure the coverage percentage on the ship surface. Experimental results demonstrate that our proposed pipeline can objectively perform a similar assessment as a qualified inspector.
