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

Recently, there has been an increase in the demand for quality control in the steel making industry. This paper proposes a vision-based method for detection of defects in the surfaces of scale-covered steel billets. Scales are formed on the surface of billets owing to the deposition of oxidized substances that are produced during manufacturing. Because of the presence of scales on the billet surface, its characteristics such as brightness and texture in the background region are inconsistent. Moreover, the similarities in the gray-levels of the defect and defect-free regions make it very difficult to accurately detect defects. In order to solve the abovementioned problems and to detect defects more effectively, we propose a new defect detection algorithm, which is based on Gabor filters. The Gabor filters are optimized using a new optimization algorithm known as univariate dynamic encoding algorithm for searches (uDEAS). The algorithm finds the minimum value of the cost function related to the energy separation criteria between the defect and the defect-free regions. Finally, the experimental results conducted on billet surface images from actual steel production line show the effectiveness of the proposed algorithm.