A New Self-Reference Image Decomposition Algorithm for Strip Steel Surface Defect Detection

Abstract

Automatic vision-based defect detection on the steel surface is a challenging task due to miscellaneous patterns of defects, low contrast between the defect and the background, and so on. Image-decomposition-based method can analyze the structure and texture to inspect the defective objects. Currently, the state of the art of image decomposition-based defect detection methods is the one guided by a given fixed template. However, a fixed template cannot be suitable for all situations. In this article, a new self-reference template-guided image decomposition algorithm for strip steel surface defect detection is developed. Combined with the statistical characteristics of a large number of defect-free images, a specific template can be built for each test defect image. Then, a total variation (TV)-based image decomposition algorithm guided by the self-reference template is developed to decompose the test image into the structural component and textural component. Moreover, the decomposition is optimized by developing a new index-gradient similarity to measure the similarity between the self-reference template and decomposed textural component. Experimental results show that the proposed method can detect various types of defects on the homogeneously textured surface, including miscellaneous defects, even for tiny defects and under the low contrast condition, and the precision, recall, and F-measure of the proposed algorithm are better than the state-of-the-art algorithms.