Automatic detection of cracks in raw steel block using Gabor filter optimized by univariate dynamic encoding algorithm for searches (uDEAS)

Investigation of Different Algorithms for Surface Defects of Steel Sheet for Quality

“…Other algorithms have been applied for this purpose such as Gabor filters [13], wavelet packet transform [14] and Shearlet conversion employed by Shunhua Liu and his colleagues for classifying surface defects of metals [1]. Given that investigating metals surface automatically is a known issue which is being investigated, but there isn't a general method to detect failures automatically [12]. Various methods for detecting and classifying surface defects of steel and other metals automatically have been presented [6,11].…”

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

“…Further, Tsai and Wu 18) also used Gabor filter selection so that the filter response energy of the normal texture was close to zero. Yun et al 9) proposed an algorithm using a Gabor filter for detecting cracks in raw steel block. The parameters of the Gabor filter were optimized by using an univariate dynamic encoding algorithm for searches (uDEAS).…”

“…7,8) An automatic defect detection system using Gabor filters optimized by a univariate dynamic encoding algorithm for searches has been developed for detecting cracks in raw steel blocks. 9) A real-time vision-based defect inspection system for coiled steel bars for high-speed applications has been proposed. 10) Although these systems were developed for steel products, it is difficult to apply them directly to pinhole detection in steel slabs, because they are optimized for a specific steel type, defect type, lighting condition, and so on.…”

Application of Shearlet transform to classification of surface defects for metals

“…For example, an algorithm combined with discrete wavelet transform and morphological analysis was developed to detect corner cracks of steel billets from oxide scales [1]; Gabor filters were used to detect thin and corner cracks in raw steel block by minimizing the cost function of energy separation criteria of defect and defect-free regions [2]; defects of structural steel plates were detected using Discrete Fourier Transform Spectral Energy and Artificial Neural Networks [3]; an approach based on 3D profile data of steel slab surfaces was developed for an automated on-line crack detection system, and morphological image processing and logistic regression based statistical classification were integrated in the system [4]; a framework with multiple views was applied to detecting flaws in aluminum castings, and information gathered from multiple views of the scene was combined for the flaw detection [5]. Although the above methods achieved high detection rates of some defects, they were restricted to some specific products or defects, and classification rates of common defects were generally limited.…”