Detection Technique for Defects on Steel Sheet Surface by Texture Analysis Using Gabor Filters with Sub-band Decomposition Considering Defect Orientation

Abstract

We propose a technique for detection of surface defects on steel sheet products by texture analysis using Gabor filters considering the defect orientation. This technique enables detection of defects with a low signal-to-noise ratio on inspection images, which are difficult to detect by conventional methods. In this technique, texture features are extracted by a group of Gabor filters with multiple scales and orientations. By statistically analyzing the extracted texture features, it is possible to detect defects as pixels with statistically abnormal texture features. The abnormality of these texture features is evaluated by Mahalanobis' distance. In this technique, sub-band decomposition adapted to the defect orientation is introduced in Gabor filtering to improve sensitivity to linear defects, which are elongated in the longitudinal (rolling) direction of steel sheets and common in steel sheet products. Experiments in which this technique was applied to images of real defects on galvanized steel sheets confirmed that sensitivity was improved compared to the case without sub-band decomposition.