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  2020, Vol. 33 Issue (12): 1135-1144    DOI: 10.16451/j.cnki.issn1003-6059.202012008
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Defect Detection Algorithm of Complex Pattern Fabric Based on Cascaded Convolution Neural Network
MENG Zhiqing1, QIU Jianshu1
1. School of Management,Zhejiang University of Technology,Hangzhou 310014

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Abstract  

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.

Key wordsFabric Defect Detection      Cascaded Convolution Neural Network      Target Detection      Defect Classification     
Received: 09 September 2020     
Fund:

National Natural Science Foundation of China(No.11871434)

Corresponding Authors: MENGZhiqing,Ph.D.,professor.Hisresearchinterestsincludedataminingandmachinelearning.   
About author:: QIU Jianshu,master student.His research interests include data mining and machine learning.
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MENG Zhiqing,QIU Jianshu. Defect Detection Algorithm of Complex Pattern Fabric Based on Cascaded Convolution Neural Network[J]. , 2020, 33(12): 1135-1144.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202012008      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I12/1135
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