Infrared image edge recognition and defect quantitative determination based on the algorithm of fuzzy C-means clustering and Canny operator

A new edge detection method based on Fuzzy C-means clustering and Canny operator was proposed to detect the defects of infrared thermal imaging with large noise, edge information ambiguity and so on. In this method, the gray scale transformation of the input infrared image was carried out, and the image was segmented, extraced, and binarizated by the Fuzzy C-means clustering; then each area was superimposed to make the edge of infrared image continuous. Finally, the image was processed by the Canny algorithm, and the edge of the infrared image was continuous. Canny operator was used to detect the edge of the image, and the defect recognition was realized. Based on the image edge detection, the relative error between calculated and actual defects position was analyzed, and the geometric size quantitative detection of defects was realized. The results show that the proposed method can detect the defect edge completely and clearly, and has higher accuracy and anti-noise ability, which is advantageous for the identification and quantitative detection of defects.