Automatic Fabric Defect Detection Using Learning-Based Local Textural Distributions in the Contourlet Domain

作者：Yapi Daniel; Allili Mohand Said^*; Baaziz Nadia

来源：IEEE Transactions on Automation Science and Engineering, 2018, 15(3): 1014-1026.

DOI：10.1109/TASE.2017.2696748

• Bayes classifier (BC)
• contourlet transform
• fabric defect detection
• mixture of generalized Gaussians (MoGG)
• texture analysis

We propose a learning-based approach for automatic detection of fabric defects. Our approach is based on a statistical representation of fabric patterns using the redundant contourlet transform (RCT). The distribution of the RCT coefficients are modeled using a finite mixture of generalized Gaussians (MoGG), which constitute statistical signatures distinguishing between defective and defect-free fabrics. In addition to being compact and fast to compute, these signatures enable accurate localization of defects. Our defect detection system is based on three main steps. In the first step, a preprocessing is applied for detecting basic pattern size for image decomposition and signature calculation. In the second step, labeled fabric samples are used to train a Bayes classifier (BC) to discriminate between defect-free and defective fabrics. Finally, defects are detected during image inspection by testing local patches using the learned BC. Our approach can deal with multiple types of textile fabrics, from simple to more complex ones. Experiments on the TILDA database have demonstrated that our method yields better results compared with recent state-of-the-art methods.

• 出版日期2018-7