Algorithm for detecting seam cracks in steel plates using a Gabor filter combination method

Abstract

Presently, product inspection based on vision systems is an important part of the steel-manufacturing industry. In this work, we focus on the detection of seam cracks in the edge region of steel plates. Seam cracks are generated in the vertical direction, and their width range is 0.2-0.6 mm. Moreover, the gray values of seam cracks are only 20-30 gray levels lower than those of the neighboring surface. Owing to these characteristics, we propose a new algorithm for detecting seam cracks using a Gabor filter combination method. To enhance the performance, we extracted features of seam cracks and employed a support vector machine classifier. The experimental results show that the proposed algorithm is suitable for detecting seam cracks.

Automated Visual Defect Detection for Flat Steel Surface: A Survey

“…Support vector machine (SVM), decision trees and neural network are classical examples in this category. As a generalized linear classifier for binary classification of data, SVM is frequently utilized to identify defective and defect-free regions [100,101]. Ghorai et al [1] hold that the performance of classifiers in defect detection depends on the feature and classifier combination.…”

Detection of Pinholes in Steel Slabs Using Gabor Filter Combination and Morphological Features

“…Gabor filter combination method combines two Gabor filtered-image based on the responses of two single Gabor filters that are designed to detect various sizes of defects. 19) We have proposed dual thresholding method to reduce the number of defect candidates. To verify the performance of the proposed method, it was evaluated using artificial pinhole defects.…”

Metal surface defect detection using iterative thresholding technique

“…An algorithm for detecting seam cracks in steel plates is illustrated in [5]. It uses Gabor Filter and adaptive double threshold method to detect cracks.…”