Magnetic Tile Surface Defect Detection Based on Texture Feature Clustering

doi:10.1007/s12204-019-2101-7

Abstract

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

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

CLC Number:

TP 391.4

LI Dan (李丹), NIU Zhongbin (牛中彬), PENG Dongxu (彭冬旭) . Magnetic Tile Surface Defect Detection Based on Texture Feature Clustering[J]. Journal of Shanghai Jiao Tong University (Science), 2019, 24(5): 663-670.

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