Magnetic tile crack defect detection based on Contourlet transform and singular value decomposition

ABSTRACT

The magnetic tile is a permanent magnet made of ferrite material, which is shaped like a tile and mainly used in permanent magnet motor. The magnetic tile image taken by camera is with dark colour and uneven background brightness and texture, and the contrast between crack defect and background is low. In this paper, a non-destructive testing method based on machine vision technology is proposed for magnetic tile crack defect detection. We propose a new crack defect detection algorithm based on Contourlet transform and singular value decomposition (SVD). The algorithm first adopted Contourlet transform to decompose the original image, and the subband coefficients were decomposed by singular value decomposition, then according to the difference of singular values gradient, the principal singular values which would be set to zero were determined. Finally, the image was reconstructed with the reconstruction coefficients that were modified by inversing SVD, the background texture was eliminated and crack defect was obtained. To verify the effectiveness and superiority of the proposed algorithm, extensive experiments were carried out and compared with the traditional algorithms. Experimental results show that the proposed method can effectively detect crack defect with the accuracy rate of 94.29%, and outperforms traditional methods.

KEYWORDS:

• Magnetic tile
• defect detection
• contourlet transform
• singular value decomposition

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Key Scientific Research Fund Project of Xihua University under Grant [Z1510220]Chunhui Project Foundation of the Education Department of China [Z2015104,Z2017074] under Grant [Z2015104]; Chunhui Project Foundation of the Education Department of China [Z2015104,Z2017074] under Grant [Z2017074]; Sichuan Science and Technology Program [2021YFG0189,2021ZHCG0015] under Grant [2021YFG0189]; and Sichuan Science and Technology Program [2021YFG0189,2021ZHCG0015] under Grant [2021ZHCG0015].