Paper Info
Title
Multistage Gan For Fabric Defect Detection
Abstract
Fabric defect detection is an intriguing but challenging topic. Many methods have been proposed for fabric defect detection, but these methods are still suboptimal due to the complex diversity of both fabric textures and defects. In this paper, we propose a generative adversarial network (GAN)-based framework for fabric defect detection. Considering existing challenges in real-world applications, the proposed fabric defect detection system is capable of learning existing fabric defect samples and automatically adapting to different fabric textures during different application periods. Specifically, we customize a deep semantic segmentation network for fabric defect detection that can detect different defect types. Furthermore, we attempted to train a multistage GAN to synthesize reasonable defects in new defect-free samples. First, a texture-conditioned GAN is trained to explore the conditional distribution of defects given different texture backgrounds. Given a novel fabric, we aim to generate reasonable defective patches. Then, a GAN-based fusion network fuses the generated defects to specific locations. Finally, the well-trained multistage GAN continuously updates the existing fabric defect datasets and contributes to the fine-tuning of the semantic segmentation network to better detect defects under different conditions. Comprehensive experiments on various representative fabric samples are conducted to verify the detection performance of our proposed method.
Year
DOI
Venue
2020
10.1109/TIP.2019.2959741
IEEE TRANSACTIONS ON IMAGE PROCESSING
Keywords
DocType
Volume
Fabric defect detection, deep learning, semantic segmentation, generative adversarial network
Journal
29
Issue
ISSN
Citations 
1
1057-7149
2
PageRank 
References 
Authors
0.37
0
5
Name
Order
Citations
PageRank
Liu Juhua184.91
Chaoyue Wang2728.54
Hai Su320.71
Bo Du41662130.01
Dacheng Tao519032747.78