Yarn-dyed Fabric Defect Detection with YOLOV2 Based on Deep Convolution Neural Networks

被引：0

作者：

Zhang, Hong-wei ^{[1
]}

Zhang, Ling-jie ^{[1
]}

Li, Peng-fei ^{[1
]}

Gu, De ^{[2
]}

机构：

[1] Xian Polytech Univ, JinHua South Rd, Xian 710048, Shaanxi, Peoples R China

[2] Jiangnan Univ, Inst Automat, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China

来源：

PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS) | 2018年

关键词：

yarn-dyed fabric; deep convolutional neural networks; defect detection; YOLOV2;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

To reduce labor costs for manual extract image features of yarn-dyed fabric defects, a method based on YOLOV2 is proposed for yarn-dyed fabric defect automatic localization and classification. First, 276 yarn-dyed fabric defect images are collected, preprocessed and labelled. Then, YOLO9000, YOLO-VOC and Tiny YOLO are used to construct fabric defect detection models. Through comparative study, YOLO-VOC is selected to further model improvement by optimize super-parameters of deep convolutional neural network. Finally, the improved deep convolutional neural network is tested for yarn-dyed fabric defect detection on practical fabric images. The experimental results indicate the proposed method is effective and low labor cost for yarn-dyed fabric defect detection.

引用

页码：170 / 174

页数：5

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