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Magnetic Tile Surface Defect Detection Based on Texture Feature Clustering

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Abstract

In the field of magnetic tile surface detection, artificial detection efficiency is low, and the traditional image segmentation algorithm cannot show good performance when the gray scale of the magnetic tile itself is small, or the image is affected by uneven illumination. In view of these questions, this paper puts forward a new clustering segmentation algorithm based on texture feature. This algorithm uses Gabor function spectra to represent magnetic tile surface texture and then uses a user-defined local product coefficient to modify Gabor energy spectra to get the center number of fuzzy C-means (FCM) clustering. Moreover, the user-defined Gabor energy spectra image is segmented by clustering algorithm. Finally, it extracts the magnetic tile surface defects according to the changes of regional gray characteristics. Experiments show that the algorithm effectively overcomes the noise interference and makes a good performance on accuracy and robustness, which can effectively detect crack, damage, pit and other defects on the magnetic tile surface.

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Authors and Affiliations

  1. College of Electrical Information and Engineering, Anhui University of Technology, Ma’anshan, 243000, Anhui, China

    Dan Li  (李 丹), Zhongbin Niu  (牛中彬) & Dongxu Peng  (彭冬旭)

Authors
  1. Dan Li  (李 丹)
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  2. Zhongbin Niu  (牛中彬)
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  3. Dongxu Peng  (彭冬旭)
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Corresponding author

Correspondence to Dan Li  (李 丹).

Additional information

Foundation item: the National Natural Science Foundation of China (Nos. 51307003 and 61601004)

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Li, D., Niu, Z. & Peng, D. Magnetic Tile Surface Defect Detection Based on Texture Feature Clustering. J. Shanghai Jiaotong Univ. (Sci.) 24, 663–670 (2019). https://doi.org/10.1007/s12204-019-2101-7

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  • DOI: https://doi.org/10.1007/s12204-019-2101-7

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