Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks

doi:10.1109/TCPMT.2018.2794540

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	Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks
	Tao, Xian1 ; Wang, Zihao 2; Zhang, Zhengtao1 ; Zhang, Dapeng1 ; Xu, De1 ; Gong, Xinyi1 ; Zhang, Lei 1
刊名	IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY
	2018-04-01
卷号	8 期号:4 页码:689-698
关键词	Convolutional Neural Network (Cnn) Defect Recognition Machine Vision Multitask Learning Spring-wire Sockets
DOI	10.1109/TCPMT.2018.2794540
文献子类	Article
英文摘要	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.; 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.
WOS关键词	VISION INSPECTION SYSTEM ; FEATURE-SELECTION ; SEGMENT DETECTOR ; EDGE-DETECTION ; CLASSIFICATION ; MACHINE ; SCALE ; TUBE
WOS研究方向	Engineering ; Materials Science
语种	英语
WOS记录号	WOS:000429960300022
资助机构	National Natural Science Foundation of China(61703399 ; 61673383 ; 61733004 ; 61421004 ; 61403382)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/21697]
专题	精密感知与控制研究中心_精密感知与控制
作者单位	1.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
推荐引用方式 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.