[1]WANG Yanchunqiu,GE Quanbo,LIU Huaping.Unsupervised learning method for surface defect detection of slate materials[J].CAAI Transactions on Intelligent Systems,2023,18(6):1344-1351.[doi:10.11992/tis.202212006]
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Unsupervised learning method for surface defect detection of slate materials

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 6 Page number: 1344-1351 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2023-11-05
Title:
Unsupervised learning method for surface defect detection of slate materials
Author(s):
WANG Yanchunqiu1;  GE Quanbo2;  LIU Huaping3
1. Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China;
2. School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China;
3. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Keywords:
slate; surface defects; defect detection; unsupervised learning; multiscale feature; semiorthogonal embedding feature; feature learning; mahalanobis distance
CLC:
TP391
DOI:
10.11992/tis.202212006
Abstract:
The surface defect detection of slate is a challenging task, particularly for small defects like edge bumps and cracks. In addition, the existence of redundant features will influence the training effect, multiscale feature learning will require multidimensional calculation, and the calculation complexity will increase. Considering the above problems, this paper proposes a method for detecting surface defects in slate materials based on unsupervised learning to solve the problem in this task effectively. First, the semiorthogonal embedding feature dimension reduction is used on the multiscale features of the image extracted using a pretraining network to reduce the effect of redundant features. Further, the time complexity of calculation is reduced through multiprocess feature learning, increasing training efficiency. Finally, the local Markov distance of the image to be measured is obtained in accordance with the training model to improve the detection performance. Relevant experiments show that the results of this method on the slate data set are superior to several advanced methods at present, and the effectiveness of this method is verified by detecting and locating surface defects in slate materials.
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