Fabric Defect Detection with Cartoon–Texture Decomposition
Automatic fabric defect detection plays an important role in textile industry. Most existing works utilize machine leaning methods to classify the fabric images with defects, however, because fabric defects are generally diverse and obscure. It is difficult to precisely identify the defects by direct image classifications. Aiming to tackle this problem, in this paper, we propose a two-stage method for automatic fabric defect detection. First, we utilize cartoon–texture decomposition to extract the features of textile structures from fabric images. Second, based on the features of cartoon textures, we build up a classifier with Deep Convolutional Neural Networks (DCNN) to distinguish the image regions containing defects, i.e., the regions of abnormal feature representation. Experimental results validate that the proposed method can precisely recognize the fabric defects and achieve good performances on the fabric images of various kinds of textiles.
KeywordsFabric defect detection Cartoon–texture decomposition Deep convolutional neural networks
This work reported here was financially supported by the National Natural Science Foundation of China (Grant No. 61573235).
- 1.Mahajan, P.M., Kolhe, S.R., Patil, P.M.: A review of automatic fabric defect detection techniques. Adv. Comput. Res. 1(2), 18–29 (2009)Google Scholar
- 6.Sayed, M.S.: Robust fabric defect detection algorithm using entropy filtering and minimum error thresholding. In: IEEE ISCAS. IEEE, pp. 1–4 (2017)Google Scholar
- 8.Meyer, Y.: Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures. University Lecture Series (2001)Google Scholar