Magnetic Tile Surface Defect Detection Based on Texture Feature Clustering

• 作 者: LI Dan NIU Zhongbin PENG Dongxu
• 机 构: College of Electrical Information and Engineering, Anhui University of Technology, Ma’anshan 243000,
• 出 处: 上海交通大学学报（英文版）
• 发表日期: 2019年5期
• 关键词: defect detection of magnetic tile Gabor functions local characteristics of gray

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.