基于改进级联R-CNN的面料疵点检测方法  

A method for fabric defect detection based on improved cascade R-CNN

作　　者：许胜宝 郑飂默[3,4] 袁德成 XU Shengbao;ZHENG Liaomo;YUAN Decheng(College of Computer Science and Technology,Shenyang University of Chemical Technology,Shenyang 110142,China;College of Information Engineering,Shenyang University of Chemical Technology,Shenyang 110142,China;Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China;Shenyang CASNC Technology Co.,Ltd.,Shenyang 110168,China)

机构地区：[1]沈阳化工大学计算机科学与技术学院,沈阳110142 [2]沈阳化工大学信息工程学院,沈阳110142 [3]中国科学院沈阳计算技术研究所,沈阳110168 [4]沈阳中科数控技术股份有限公司,沈阳110168

出　　处：《现代纺织技术》2022年第2期48-56,共9页Advanced Textile Technology

基　　金：国家重点研发计划“智能机器人专项”项目(2018YFB1308803)。

摘　　要：由于布匹疵点种类分布不均,部分疵点具有极端的宽高比,而且小目标较多,导致检测难度大,因此提出一种改进级联R-CNN的布匹疵点检测方法。针对小目标问题,在R-CNN部分采用在线难例挖掘,加强对小目标的训练;针对布匹疵点极端的长宽比,在特征提取网络中采用了可变形卷积v2来代替传统的正方形卷积,并结合布匹特征重新设计边界框比例。最后采用完全交并比损失作为边界框回归损失,获取更精确的目标边界框。结果表明:对比改进前的模型,改进后的模型预测边界框更加精确,对小目标的疵点检测效果更好,在准确率上提升了3.57%,平均精确度均值提升了6.45%,可以更好地满足面料疵点的检测需求。To solve the difficult detection problem due to the uneven distribution of different fabric defects,extreme aspect ratios existing in some defects,and a large number of small targets,a method for fabric defect detection based on improved cascade R-CNN was proposed.The difficult examples were mined online in R-CNN part to strengthen small target training.To address the issue of the extreme aspect ratio of fabric defects,the traditional square volume in the feature extraction network was replaced by deformable convolution v2.The scale of the bounding box was redesigned according to the characteristics of the fabric.Finally,the complete intersection over union loss was adopted as the bounding box regression loss,and a more accurate target bounding box was obtained.The experimental results indicated that the improved model was more accurate in predicting the bounding box than that before improvement,and it achieved a better effect on small target detection.The accuracy was improved by 3.57%,and the average accuracy was improved by 6.45%.Therefore,it can better meet the requirements of fabric defect detection.

关 键 词：级联R-CNN 面料疵点 检测 可变形卷积v2 在线难例挖掘 完全交并比损失 

分 类 号：TS101.8[轻工技术与工程—纺织工程] TP391[轻工技术与工程—纺织科学与工程]