A Simple Guidance Template-Based Defect Detection Method for Strip Steel Surfaces

Abstract

Automatic defect detection on strip steel surfaces is a challenging task in computer vision, owing to miscellaneous patterns of defects, disturbance of pseudodefects, and random arrangement of gray-level in background. In this paper, a novel template establishment is presented. Further, a simple guidance template-based algorithm for strip steel surface defect detection is proposed. First, a large number of defect-free images are collected to obtain the statistical characteristic of normal textures. Second, for each given test image, the initial template is built according to the statistical characteristic and the size of test image. Then, a sorting operation is applied to the given test image. Further, by updating the initial template, a unique guidance template is generated based on specific intensity distribution of the sorted test image. So far, the background of each test image is approximately reconstructed in the guidance template. Finally, based on pixel-wise detection, the defects can be located accurately by subtraction operation between the guidance template and sorted test image, reverse sorting operation, and adaptive threshold determination. Experimental results show that the proposed method is both efficient and effective. It achieves a better average detection rate of 96.2% on a data set including 1500 test images.