Various corrosion characteristics such as rust spots and cracks will appear on the surface of metal plates after corrosion.Their corrosion degree can be determined by corrosion characteristics.At present,the corrosion degree of metal plates is mainly judged by manual visual inspection.But it has many non-ignorable shortcomings such as low consistency and low efficiency etc.In this paper,the RGB values of image pixels of corroded metal sheets were collected and then clustered by means of K-means++clustering algorithm,afterwards the relevant corroded-and uncorroded-regions were separat-ed.Whether corrosion occurred or not was judged in each cluster area by means of image structural simi-larity index SSIM.The results show that setting the number of clustering centers'k'to 5 can effectively delineate each clustering area based on the image color distribution.Compared to peak signal-to-noise ratio and mean square error,the structural similarity index SSIM is strongly correlated with the occur-rence of erosion.Setting the SSIM index threshold at 0.95 can effectively judge whether erosion occurred in each cluster area.Compared to manually dividing corrosion areas based on pixel color,our method had a higher identification efficiency and an accuracy of not less than 90％.This research can be applied to automate the evaluation of the corrosion degree of metal sheets after environmental testing.
关键词
腐蚀评估/图像处理/K-means++算法/聚类/图像相似度/智能诊断/腐蚀面积
Key words
corrosion evaluation/image processing/K-means++algorithm/clustering/image similar-ity/intelligent diagnosis/corrosion area
维普
万方数据