In defect location and classification of complex colored fabric,it is difficult to locate and classify defects in the cloth with complex and changeable background information.To solve this problem,a defect detection algorithm of complex pattern fabric based on cascaded convolution neural network is proposed.Firstly,the backbone feature extraction network based on two-way residual is applied to extract and fuse features from defect map and template map.Then,a density clustering frame producer is designed to guide the design of pre inspection frame for regional candidate networks in the framework.Finally,the cascaded regression method is utilized to locate and classify the defects accurately.The cloth image data collected from industrial field is adopted for training and prediction.The final results show that the proposed algorithm achieves high accuracy and recall rate.
[1] SO Y,KIM J,HUANG H.Fabric Defect Detection Using a Hybrid Particle Swarm Optimization-Gravitational Search Algorithm and a Gabor Filter.Journal of the Optical Society of America A,2020,37(7):1229-1235.
[2] ZHANG K B,YAN Y D,LI P F,et al.Fabric Defect Detection Using Salience Metric for Color Dissimilarity and Positional Aggregation.IEEE Access,2018,6:49170-49181.
[3] TONG L,WONG W K,KWONG C K.Fabric Defect Detection for Apparel Industry:A Nonlocal Sparse Representation Approach.IEEE Access,2017,5:5947-5964.
[4] YAN H,PAYNABAR K,SHI J J.Anomaly Detection in Images with Smooth Background via Smooth-Sparse Decomposition.Technometrics,2017,59(1):102-114.
[5] ZHOU J,WANG J.Unsupervised Fabric Defect Segmentation Using Local Patch Approximation.The Journal of the Textile Institute,2016,107(6):800-809.
[6] HUANGPENG Q Z,ZHANG H,ZENG X R,et al.Automatic Vi-sual Defect Detection Using Texture Prior and Low-Rank Representation.IEEE Access,2018,6:37965-37976.
[7] LI P,LIANG J L,SHEN X B,et al.Textile Fabric Defect Detection Based on Low-Rank Representation.Multimedia Tools and Applications,2019,78(1):99-124.
[8] MEI S,WANG Y D,WEN G J.Automatic Fabric Defect Detection with a Multi-scale Convolutional Denoising Autoencoder Network Model.Sensors,2018,18(4):1061-1081.
[9] LI Y D,ZHAO W G,PAN J H.Deformable Patterned Fabric Defect Detection with Fisher Criterion-Based Deep Learning.IEEE Transactions on Automation Science and Engineering,2017,14(2):1256-1264.
[10] JIA L,CHEN C,LIANG J Z,et al.Fabric Defect Inspection Based on Lattice Segmentation and Gabor Filtering.Neurocomputing,2017,238:84-102.
[11] TONG L,WONG W K,KWONG C K.Differential Evolution-Based Optimal Gabor Filter Model for Fabric Inspection.Neurocomputing,2016,173:1386-1401.
[12] OUYANG W B,XU B G,HOU J,et al.Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network.IEEE Access,2019,7:70130-70140.
[13] QU T,ZOU L,ZHANG Q L,et al.Defect Detection on the Fabric with Complex Texture via Dual-Scale Over-Complete Dictionary.The Journal of the Textile Institute,2016,107(6):743-756.
[14] DAI J F,LI Y,HE K M,et al.R-FCN:Object Detection via Region-Based Fully Convolutional Networks//Proc of the 30th International Conference on Neural Information Processing Systems.Cambridge,USA:The MIT Press,2016:379-387.
[15] LIN T Y,GOYAL P,GIRSHICK R,et al.Focal Loss for Dense Object Detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(2):318-327.
[16] LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single Shot Multibox Detector//Proc of the European Conference on Computer Vision.Berlin,Germany:Springer,2016:21-37.
[17] REDMON J,DIVVALA S,GIRSHICK R,et al.You Only Look Once:Unified,Real-Time Object Detection//Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2016:779-788.
[18] REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks//Proc of the 28th International Conference on Neural Information Proce-ssing Systems.Cambridge,USA:The MIT Press,2015:91-99.
[19] CAI Z W,VASCONCELOS N.Cascade R-CNN:Delving into High Quality Object Detection//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington,
USA:IEEE,2018:6154-6162.
[20] LIN T Y,DOLLAR P,GIRSHICK R,et al.Feature Pyramid Networks for Object Detection//Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2017:936-944.
[21] RODRIGUEZ A,LAIO A.Clustering by Fast Search and Find of Density Peaks.Science,2014,344(6191):1492-1496.
2020, Vol. 33 
