On-line detection of the dyed and printed fabric defects by multi-features evidence learning and enhancement in spatiotemporal domain

A novel method of defects detection for dyed and printed fabrics is presented,which is based on multi-features evidence learning and enhancement in spatiotemporal domain.It′s a general solution to many real-time surface inspection issues.The mutual compensation of multi-features is used to enhance the defects evidence,and history information of the doubted patches in video sequence is also applied to help checking out what are the true defects.The main idea is to find out the unknown defects by comparing the extracted surface features of the known defect-free fabric with those of the fabric being examined.This inspection procedure is divided into two stages: one for the roll-style multi-features learning of the known defect-free textile,the other for realtime surface inspection.Many experiment results of the on-line inspection show that the efficient detection speed reaches 55 frames per second to the image sequence((1 024)×393 pixels) for dyed and printed fabrics of single-color,with a correct dynamic check out rate on surface defects above 95%.