Research on textile defects detection based on improved generative adversarial network

Abstract

Fabric defects seriously affect the textile industry in China. Given that traditional manual detection methods have low efficiency and poor accuracy, using automatic textile defect detection methods is urgently needed. A fabric defect detection method based on an improved generative adversarial network is thus developed to address the shortage of fabric defect samples. This method learns to reconstruct the fabric image in an unsupervised manner and locates the defect areas based on the differences between the original image and the reconstruction. Afterward, the defect-related features are extracted from these areas to further recognize specific fabric defects. The central loss constraint is introduced to improve the recognition performance of this method, and lightweight processing is applied to guarantee its real-time operation in embedded systems. The application of this method is then evaluated on the publicly available Tianchi dataset. Both quantitative and qualitative results show that the proposed method can accurately detect fabric defects.