The exploration of computer vision applications for fabric defect detection has immense potential value.However,current relevant research in this area has primarily focused on detection models that aim for high detect...The exploration of computer vision applications for fabric defect detection has immense potential value.However,current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency,while neglecting the practical industrial production requirements.Therefore,we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network(CNN)features(i.e.,Faster R-CNN)for defect detection,defect localization and detection model evaluation.In addition,the proposed intelligent system incorporates novel approaches,such as a rearranged fabric dataset,anomaly detection,recommended clipping region division,and a replenishment device.This study illustrates an example of artificial intelligence(AI)-driven automated technology in fabric manufacturing.The accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related work.Experimental results demonstrate that the proposed approach achieves comparable performance to other models,while significantly reducing computational resource requirements.The potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted,which is likely to have relevant implications for the textile industry.展开更多
基金supported in part by the Free Exploration Fund of Guangxi Key Laboratory of Structure-Activity Relationship of Electronic Information Materials(No.211019-Z)Guangxi Natural Science Foundation(No.2024GXNSFAA010493)+3 种基金Innovation Project of GUET Graduate Education(No.2022YCXS193)Chinesisch-Deutsche Kooperationsgruppe(No.GZ1528)National Key Research and Development Program(No.SQ2022YFB4000136)Science and Technology Development Project of Guilin(No.20210102-4).
文摘The exploration of computer vision applications for fabric defect detection has immense potential value.However,current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency,while neglecting the practical industrial production requirements.Therefore,we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network(CNN)features(i.e.,Faster R-CNN)for defect detection,defect localization and detection model evaluation.In addition,the proposed intelligent system incorporates novel approaches,such as a rearranged fabric dataset,anomaly detection,recommended clipping region division,and a replenishment device.This study illustrates an example of artificial intelligence(AI)-driven automated technology in fabric manufacturing.The accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related work.Experimental results demonstrate that the proposed approach achieves comparable performance to other models,while significantly reducing computational resource requirements.The potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted,which is likely to have relevant implications for the textile industry.
