Defect Detection Algorithm of Patterned Fabrics Based on Convolutional Neural Network  

作　　者：XU Yang FEI Libin YU Zhiqi SHENG Xiaowei 徐洋;费利斌;余智祺;盛晓伟(College of Mechanical Engineering,Donghua University,Shanghai201620,China)

机构地区：[1]College of Mechanical Engineering,Donghua University,Shanghai201620,China

出　　处：《Journal of Donghua University(English Edition)》2021年第1期36-42,共7页东华大学学报（英文版）

基　　金：National Key Research and Development Project,China(No.2018YFB1308800)。

摘　　要：The background pattern of patterned fabrics is complex,which has a great interference in the extraction of defect features.Traditional machine vision algorithms rely on artificially designed features,which are greatly affected by background patterns and are difficult to effectively extract flaw features.Therefore,a convolutional neural network(CNN)with automatic feature extraction is proposed.On the basis of the two-stage detection model Faster R-CNN,Resnet-50 is used as the backbone network,and the problem of flaws with extreme aspect ratio is solved by improving the initialization algorithm of the prior frame aspect ratio,and the improved multi-scale model is designed to improve detection of small defects.The cascade R-CNN is introduced to improve the accuracy of defect detection,and the online hard example mining(OHEM)algorithm is used to strengthen the learning of hard samples to reduce the interference of complex backgrounds on the defect detection of patterned fabrics,and construct the focal loss as a loss function to reduce the impact of sample imbalance.In order to verify the effectiveness of the improved algorithm,a defect detection comparison experiment was set up.The experimental results show that the accuracy of the defect detection algorithm of patterned fabrics in this paper can reach 95.7%,and it can accurately locate the defect location and meet the actual needs of the factory.

关 键 词：patterned fabrics defect detection convolutional neural network(CNN) multi-scale model cascade network 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TS101[自动化与计算机技术—计算机科学与技术]