CASIA OpenIR  > 精密感知与控制研究中心  > 精密感知与控制
Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks
Tao, Xian1; Wang, Zihao2; Zhang, Zhengtao1; Zhang, Dapeng1; Xu, De1; Gong, Xinyi1; Zhang, Lei1
Source PublicationIEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY
2018-04-01
Volume8Issue:4Pages:689-698
SubtypeArticle
AbstractAs a critical electrical connector component in the modern industrial environment, spring-wire sockets and their manufacture quality are closely relevant to equipment safety. These types of defects in a component are difficult to properly distinguish due to the defect similarity and diversity. In such cases, defect types can only be determined using cumbersome human visual inspection. To satisfy the requirements of quality control, a machine vision apparatus for component inspection is presented in this paper. With a brief description of the apparatus system design, our emphasis is put on the defect recognition algorithm. A multitask convolutional neural network (CNN) is proposed for detecting those ambiguous defects. Compared with the image processing method in machine vision, the defect inspection problem is converted into object detection and classification problems. Instead of breaking it down into two separate tasks, we jointly handle both aspects in a single CNN. In addition, data augmentation methods are discussed to analyze their effects on defects recognition. Successful inspection results using the presented model are obtained using challenging real-world defect image data gathered from a spring-wire socket module inspection line in an industrial plant.; As a critical electrical connector component in the modern industrial environment, spring-wire sockets and their manufacture quality are closely relevant to equipment safety. These types of defects in a component are difficult to properly distinguish due to the defect similarity and diversity. In such cases, defect types can only be determined using cumbersome human visual inspection. To satisfy the requirements of quality control, a machine vision apparatus for component inspection is presented in this paper. With a brief description of the apparatus system design, our emphasis is put on the defect recognition algorithm. A multitask convolutional neural network (CNN) is proposed for detecting those ambiguous defects. Compared with the image processing method in machine vision, the defect inspection problem is converted into object detection and classification problems. Instead of breaking it down into two separate tasks, we jointly handle both aspects in a single CNN. In addition, data augmentation methods are discussed to analyze their effects on defects recognition. Successful inspection results using the presented model are obtained using challenging real-world defect image data gathered from a spring-wire socket module inspection line in an industrial plant.
KeywordConvolutional Neural Network (Cnn) Defect Recognition Machine Vision Multitask Learning Spring-wire Sockets
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TCPMT.2018.2794540
WOS KeywordVISION INSPECTION SYSTEM ; FEATURE-SELECTION ; SEGMENT DETECTOR ; EDGE-DETECTION ; CLASSIFICATION ; MACHINE ; SCALE ; TUBE
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61703399 ; 61673383 ; 61733004 ; 61421004 ; 61403382)
WOS Research AreaEngineering ; Materials Science
WOS SubjectEngineering, Manufacturing ; Engineering, Electrical & Electronic ; Materials Science, Multidisciplinary
WOS IDWOS:000429960300022
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21697
Collection精密感知与控制研究中心_精密感知与控制
Affiliation1.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
2.Civil Aviat Univ China, Sinoeuropean Inst Aviat Engn, Tianjin 300300, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Tao, Xian,Wang, Zihao,Zhang, Zhengtao,et al. Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks[J]. IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY,2018,8(4):689-698.
APA Tao, Xian.,Wang, Zihao.,Zhang, Zhengtao.,Zhang, Dapeng.,Xu, De.,...&Zhang, Lei.(2018).Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks.IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY,8(4),689-698.
MLA Tao, Xian,et al."Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks".IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY 8.4(2018):689-698.
Files in This Item: Download All
File Name/Size DocType Version Access License
tao2018.pdf(4279KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Tao, Xian]'s Articles
[Wang, Zihao]'s Articles
[Zhang, Zhengtao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Tao, Xian]'s Articles
[Wang, Zihao]'s Articles
[Zhang, Zhengtao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Tao, Xian]'s Articles
[Wang, Zihao]'s Articles
[Zhang, Zhengtao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: tao2018.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.