Journal of Shanghai Jiao Tong University (Science) ›› 2019, Vol. 24 ›› Issue (5): 663-670.doi: 10.1007/s12204-019-2101-7

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Magnetic Tile Surface Defect Detection Based on Texture Feature Clustering

LI Dan (李丹), NIU Zhongbin (牛中彬), PENG Dongxu (彭冬旭)   

  1. (College of Electrical Information and Engineering, Anhui University of Technology, Ma’anshan 243000, Anhui, China)
  • 出版日期:2019-10-08 发布日期:2019-09-27
  • 通讯作者: LI Dan (李丹) E-mail: lanldok@163.com

Magnetic Tile Surface Defect Detection Based on Texture Feature Clustering

LI Dan (李丹), NIU Zhongbin (牛中彬), PENG Dongxu (彭冬旭)   

  1. (College of Electrical Information and Engineering, Anhui University of Technology, Ma’anshan 243000, Anhui, China)
  • Online:2019-10-08 Published:2019-09-27
  • Contact: LI Dan (李丹) E-mail: lanldok@163.com

摘要: 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.

关键词: defect detection of magnetic tile, Gabor functions, local characteristics of gray scale, fuzzy C-means (FCM) clustering

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.

Key words: defect detection of magnetic tile, Gabor functions, local characteristics of gray scale, fuzzy C-means (FCM) clustering

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