Multiscale GAN With Region Adaptive Schemes for Online Inspection of Fabric Flexographic Printing Labels

Online inspection of flexographic printing labels (FPLs) is significant for preventing batch misprints and ensuring mass production quality. In this article, an online inspection system is designed for fabric FPLs, in which a multiscale generative adversarial network (GAN) framework with several region adaptive schemes is proposed to inspect the fabric FPL images acquired by the designed hardware system. In order to address the scarcity of defective samples in real production, the framework only employs qualified FPL samples to train the GAN template generators for generating the templates. Different from previous template generators, the framework incorporates skip connections and a multiscale architecture to improve the template representation ability. In order to deal with tensions in the FPL substrates and to capture subtle defects, several region adaptive schemes are designed for the inspection stage, involving statistical region subtracter (SRS), region adaptive thresholding (RAT), and region defect assessment (RDA). Specifically, during inspection, the inspected FPL is adaptively divided into the character and background regions, which are dealt with by these region adaptive schemes. Experimental results demonstrate that the proposed framework achieves the fabric FPL inspection performance of 0.1% omission rate and 1.8% error rate at a reasonable speed of 45 ms per sample, which can meet the online inspection requirements for real production.