基于电子样稿的柔印首件“粗-精”检测方法

Coarse-to-fine Inspection for Flexo First Item Based on the Electronic Sample

为了解决柔印首件检验没有基准织物图像作为参照的难点,该文提出一种以电子样稿为参照物的柔印首件“粗-精”检测方法,主要分为粗匹配、精匹配和缺陷检测3个阶段。首先,针对电子样稿与印刷首件内容粗细不一、灰度特性差异大、柔印内容重复率高等问题,融合超点(SuperPoint)与强力匹配(SuperGlue)方法进行粗匹配。然后,针对柔印过程版材伸缩、弯曲引发柔印内容局部偏移的问题,采用归一化互相关(NCC)法微调样稿字符,实现精匹配。最后,提出约束聚类的方法将缺陷检测问题转化为电子样稿与柔印首件差异最小化的问题。对比实验表明,该文方法的柔印首件检测性能要优于其他织物印刷品缺陷检测方法,其漏检率为0,误检率为1.3%,平均Dice系数为0.941,且检测时间仅为2.761 s/pcs,满足实际工程的需求。

In order to solve the problem that there is no real reference of the fabric image in the flexo first item inspection,a coarse-to-fine inspection method of flexo first item is proposed based on electronic samples,which is mainly divided into three stages:coarse matching,fine matching and defect detection.First,since different thickness of the characters,large differences in gray characteristics,and high repetition of flexo content inherently exist in the electronic sample and the flexo first item,the SuperGlue with SuperPoint are employed for rough matching.Then,a Normalized Cross-Correlation(NCC)-based method is used to fine-tune the electronic sample characters for fine matching,which can deal with local offsets of flexo content are caused by plate expansion and bending in flexo printing process.Finally,a constrained clustering method is proposed to transform the defect detection problem into the problem of minimizing the difference between electronic sample and flexo first item.Comparison experiments show that the proposed method can achieve better inspection performance for flexo first item,with the missed detection rate of 0,the false detection rate of 1.3%,the average Dice coefficient of 0.941,and the inspection time of 2.761 s/pcs.This promising inspection indicates that the proposed method can be well employed in real industries.