Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm

Online defect detection system is a necessary technical measure and important means for large-scale industrial printing production. It is effective to reduce artificial detection fatigue and improve the accuracy and stability of industry printing line. However, the existing defect detection algorith...

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Main Authors: Bangchao Liu, Youping Chen, Jingming Xie, Bing Chen
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2021/2036466
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author Bangchao Liu
Youping Chen
Jingming Xie
Bing Chen
author_facet Bangchao Liu
Youping Chen
Jingming Xie
Bing Chen
author_sort Bangchao Liu
collection DOAJ
description Online defect detection system is a necessary technical measure and important means for large-scale industrial printing production. It is effective to reduce artificial detection fatigue and improve the accuracy and stability of industry printing line. However, the existing defect detection algorithms are mainly developed based on high-quality database and it is difficult to detect the defects on low-quality printing images. In this paper, we propose a new multi-edge feature fusion algorithm which is effective in solving this problem. Firstly, according to the characteristics of sheet-fed printing system, a new printing image database is established; compared with the existing databases, it has larger translation, deformation, and uneven illumination variation. These interferences make defect detection become more challenging. Then, SIFT feature is employed to register the database. In order to reduce the number of false detections which are caused by the position, deformation, and brightness deviation between the detected image and reference image, multi-edge feature fusion algorithm is proposed to overcome the effects of these disturbances. Lastly, the experimental results of mAP (92.65%) and recall (96.29%) verify the effectiveness of the proposed method which can effectively detect defects in low-quality printing database. The proposed research results can improve the adaptability of visual inspection system on a variety of different printing platforms. It is better to control the printing process and further reduce the number of operators.
format Article
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institution Kabale University
issn 1076-2787
1099-0526
language English
publishDate 2021-01-01
publisher Wiley
record_format Article
series Complexity
spelling doaj-art-136c5a335a124da99d154152132fa2212025-02-03T01:27:08ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/20364662036466Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion AlgorithmBangchao Liu0Youping Chen1Jingming Xie2Bing Chen3School of Mechanical Science and Engineering, State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Mechanical Science and Engineering, State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Mechanical Science and Engineering, State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Mechanical Science and Engineering, State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaOnline defect detection system is a necessary technical measure and important means for large-scale industrial printing production. It is effective to reduce artificial detection fatigue and improve the accuracy and stability of industry printing line. However, the existing defect detection algorithms are mainly developed based on high-quality database and it is difficult to detect the defects on low-quality printing images. In this paper, we propose a new multi-edge feature fusion algorithm which is effective in solving this problem. Firstly, according to the characteristics of sheet-fed printing system, a new printing image database is established; compared with the existing databases, it has larger translation, deformation, and uneven illumination variation. These interferences make defect detection become more challenging. Then, SIFT feature is employed to register the database. In order to reduce the number of false detections which are caused by the position, deformation, and brightness deviation between the detected image and reference image, multi-edge feature fusion algorithm is proposed to overcome the effects of these disturbances. Lastly, the experimental results of mAP (92.65%) and recall (96.29%) verify the effectiveness of the proposed method which can effectively detect defects in low-quality printing database. The proposed research results can improve the adaptability of visual inspection system on a variety of different printing platforms. It is better to control the printing process and further reduce the number of operators.http://dx.doi.org/10.1155/2021/2036466
spellingShingle Bangchao Liu
Youping Chen
Jingming Xie
Bing Chen
Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm
Complexity
title Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm
title_full Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm
title_fullStr Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm
title_full_unstemmed Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm
title_short Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm
title_sort industrial printing image defect detection using multi edge feature fusion algorithm
url http://dx.doi.org/10.1155/2021/2036466
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AT youpingchen industrialprintingimagedefectdetectionusingmultiedgefeaturefusionalgorithm
AT jingmingxie industrialprintingimagedefectdetectionusingmultiedgefeaturefusionalgorithm
AT bingchen industrialprintingimagedefectdetectionusingmultiedgefeaturefusionalgorithm