An Infrared Thermal Image Processing Framework for Defect Detection of a Metal Part with Rough Surface
We propose an infrared thermal image processing framework based on a modified fuzzy c-means clustering algorithm with revised similarity measure in this paper. The framework can realize the defect detection of a metal part with rough surface. Firstly, a comprehensive method is used to preprocess infrared thermal image. Secondly, the preprocessed image is segmented using modified fuzzy c-means clustering algorithm with revised similarity measure. Finally, taking the average gray level of each cluster in the original gray scale image as a feature, defect cluster is recognized. Experimental result shows that the proposed framework has very promising performance and can obtain precise information of defects on a metal part with rough surface.
J. Xie et al., "An Infrared Thermal Image Processing Framework for Defect Detection of a Metal Part with Rough Surface", Applied Mechanics and Materials, Vols. 229-231, pp. 1356-1360, 2012