Fabric Defect Detection Using Deep Convolution Neural Network. (September 2021)

• Record Type: Journal Article
• Authors: Fan, Junjun; Wong, Wai Keung; Wen, Jiajun; Gao, Can; Mo, Dongmei; Lai, Zhihui
• Is Part Of: AATCC Journal of Research. Volume 8(2021)Supplement 1
• Page Start: 143
• Page End: 150
• Publication Date: 2021-09
• Subjects: Computer Vision -- Deep Convolutional Neural Network -- Fabric Defect Detection -- Variational Autoencoder
• DOI: 10.14504/ajr.8.S1.18
• Languages: English
• ISSNs: 2472-3444

Abstract

Abstract: Fabric defect detection plays an increasingly important role in the industrial automation application for fabric production, but how to detect defects rapidly and accurately is still challenging. In this study, we propose a powerful fabric defect detection method using a hybrid of convolutional neural network (CNN) and variational autoencoder (VAE). The convolutional layers are used for extracting fabric image pattern features and the variational autoencoder is used for modeling the latent characteristics and inferring a reconstruction. The defect positions can be detected by the differences between the original image and the reconstruction image. The proposed method is validated on public patterned fabric datasets. The experimental results demonstrate that the proposed model can achieve outstanding performance in both image level and pixel level defect detection.