Detection of Periodic Defects Using Dual-Light Switching Lighting Method on the Surface of Thick Plates

Abstract

Currently, automated inspection algorithms are widely used to ensure high-quality products and achieve high productivity in the steel making industry. In this paper, we propose a vision-based method to detect periodic defects in the surface of thick plates. To minimize the influence of a non-uniform surface property and improve the accuracy of the detection rate, a detection method based on dual-light switching lighting (DLSL) proposed. In general, single lighting (SL) methods cannot well represent the steel surface because the surface features are not uniform and strongly vary according to lighting conditions. In the DLSL method, defective regions are represented by a black and white pattern, regardless of shape, size, or orientation. Therefore, defects can be found by the black and white patterns in the corresponding images. Gabor filtering was used to find defective regions and reduce the false positive rates. To find the periodic candidates of defects, we process the period searching using manufacturing information. To identify periodic defects from among the defect candidates, we use "similarity of shapes" features with a support vector machine (SVM) classifier. The experimental results show that the proposed algorithm is effective at detecting periodic defects on the surface of thick plates.