Steel Surface Defect Detection Using Glcm, Gabor Wavelet, Hog, And Random Forest Classifier
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Abstract
In the current context of market opening, quality control is essential in the field of steel production where quality is combined with the reduction in manufacturing costs. This control can be described as a set of systems deployed to verify and maintain the desired level of quality. In the manufacturing processes of steel products, great importance is allocated to the surface condition and the possibilities of its inspection, production in progress. Simple visual inspection is unable to follow the product which is generally in motion, and even with a reduced speed of the process, the inspection of the surface can only be carried out as a sampling, which is not exhaustive. Inspection at the end of the process, for its part, could not be the ideal solution, since it will only allow the history of the process to be traced, and information on its trends. Therefore, defects in the final product, which are not detected and corrected, lead to the downgrading of products and incur additional costs.Automatic detection and recognition of surface faults in the metallurgical industry are objectives for which new technologies are being implemented, to obtain greater quality control and competitive advantages in production. To this end, a machine learning-based system is presented in this paper for the inspection of steel surface defects using various feature extraction techniques; Gray-Level Co-Occurrence Matrix (GLCM), Gabor Wavelet, and Histogram of Oriented Gradients (HOG). The classification of extracted features is accomplished by Random Forest Classifier.
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