基于改进类激活映射的织物疵点检测  

FABRIC DEFECT DETECTION BASED ON IMPROVED CLASS ACTIVATION MAPPING

作　　者：李飞龙 李敏 何儒汉 崔树芹 Li Feilong;Li Min;He Ruhan;Cui Shuqin(School of Mathematics and Computer Science,Wuhan Textile University,Wuhan 430200,Hubei,China)

机构地区：[1]武汉纺织大学数学与计算机学院,湖北武汉430200

出　　处：《计算机应用与软件》2024年第1期246-252,共7页Computer Applications and Software

基　　金：湖北省教育厅科技项目(D20161605)。

摘　　要：为实现弱监督条件下的织物疵点检测,提出一种基于改进类激活映射(Class activation mapping,CAM)的疵点检测方法。在卷积神经网络中加入SE模块,并将深层和浅层卷积层进行结合,以此提高网络的分类性能;为了提高疵点定位的准确性,将两种分辨率的类激活图进行融合来生成改进的类激活图。实验结果表明,该算法对无疵点、孔、污渍和纱疵四个类别织物图像的识别准确率达到了96.88%,并且在数据集只有图像级标注的情况下,实现了织物疵点的定位。This paper proposes a fabric detect defection method based on improved class activation mapping(CAM)to realize the fabric defect detection under weak supervised condition.This paper added squeeze-excitation(SE)block to CNN and combined the deep layer and the shallow layer together to improve the classification performance of the network.In order to obtain more accurate localization results,an improved class activation map was generated by combining the class activation maps of two resolutions.Experimental results show that the recognition accuracy of the proposed algorithm is 96.88%for four categories of fabric images,including no defects,holes,stains and yarn defects.Meanwhile,it can locate fabric defects accurately when there is only image-level labeling available in the data set.

关 键 词：疵点检测 弱监督 织物 类激活映射 卷积神经网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]