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Classification of Surface Defects Based on Improved Gabor Filter

Due to the influence of the production environment and raw materials, the surface texture of the strip steel has complexity and diversity, and there is a lot of interference in the texture background. Therefore, it is very difficult to classify the surface texture defects of the strip. To this end, a Gabor filter optimized based on differential evolution algorithm is proposed. First, anisotropic filtering is used to suppress false edges among defects. Then, the maximum internal difference of the filtering results between different defects is taken as the objective function, and the parameters in the Gabor filter are optimized using the differential evolution algorithm. Finally, support vector machine (SVM) is used to classify the filtered results. Experimental results show that this method has good discrimination and robustness. Aiming at the common surface defects of strip steel: punching, staining, scraping, oxide scale, and scarring, the final classification accuracy can reach 97%, which is higher than the traditional method.