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

Mohamed Ben Gharsallah () and Ezzedine Ben Braiek
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Mohamed Ben Gharsallah: Higher National Engineering School of Tunis ENSIT
Ezzedine Ben Braiek: Higher National Engineering School of Tunis ENSIT

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 4, No 8, 1025-1041

Abstract: Abstract 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.

Keywords: Silicon steel images; Texture; Defect detection; Anisotropic diffusion; Image filtering; Saliency map (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10845-020-01601-1

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