基于级联卷积神经网络的复杂花色布匹瑕疵检测算法

doi:10.16451/j.cnki.issn1003-6059.202012008

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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 words： Fabric 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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Cite this article:

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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