Computer aided manufacturing method for surface silicon steel inspection based on an efficient anisotropic diffusion algorithm

Quality control in silicon steel manufacturing process is a crucial step. The application of image processing techniques is very useful in steel inspection and manufacturing. It has established to be the most reliable and promising solution for the development of an automatic defect detection. Since the surface of the silicon steel strip has a cluttered background and defects with small sizes, flaws detection becomes a complex task. In this paper a novel rapid algorithm based on anisotropic diffusion and saliency map is proposed for detection of defects in images of hot rolled silicon steel. The algorithm first adopted a saliency map to enhance defects. Then the computed saliency map was employed in the anisotropic diffusion coefficient function as an orientation guide of the diffusion flow. The aim behind using salient feature is that a small defect can frequently attract attention of human eyes which permits to identify defects in high textured image. Finally, the defects were extracted using a local threshold operator. To verify the validity of the proposed algorithm, extensive experiments were realized on an image database of silicon steel strip then a comparison with traditional diffusion algorithms was given. Experimental results show that this method achieves accuracy and outperforms traditional methods in terms of accuracy and robustness.

Automated summary

Quality control in silicon steel manufacturing is crucial, and image processing techniques have proven useful for defect detection. A novel algorithm based on anisotropic diffusion and saliency map was proposed for defect detection in hot rolled silicon steel images, showing improved accuracy and robustness compared to traditional methods.