Knowledge Commons of Institute of Automation,CAS
Visual Defect Inspection for Deep-Aperture Components With Coarse-to-Fine Contour Extraction | |
Gong, Xinyi1,2; Su, Hu1,2; Xu, De1,2; Zhang, Jiabin1,2; Zhang, Lei1,2; Zhang, Zhengtao1,2 | |
发表期刊 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
ISSN | 0018-9456 |
2020-06-01 | |
卷号 | 69期号:6页码:3262-3274 |
摘要 | This paper investigates automatic quality inspection for the components with a small diameter and deep aperture. An automatic pick-and-place system is constructed, which employs an endoscope to achieve better image quality aiming at the characteristics of the component. A coarse-to-fine contour extraction algorithm with four steps is presented to inspect the component's quality. First, approximate locations of the targets are estimated using faster region-based convolutional neural networks (faster RCNN). Second, the corresponding edge image is obtained by using the multiscale probability boundary (mPb) detector. Third, edge enhancement is performed, which is based on the Brownian motion model. Fourth, the corresponding contours are finely extracted by edge grouping. A shape analyzing algorithm is utilized to classify the components based on the extracted contours. Comparison experiments fully demonstrate the superiority of the proposed inspection method over existing methods. Meanwhile, successful inspection results on challenging real-world image data prove that the system is of practical significance to industrial applications. |
关键词 | Coarse-fine positioning deep-hole component defect inspection edge grouping image processing |
DOI | 10.1109/TIM.2019.2928347 |
关键词[WOS] | COMPLETION ; BOUNDARIES ; MODEL ; SHAPE |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2017YFB1302303] ; National Natural Science Foundation of China[61503378] ; National Natural Science Foundation of China[61733004] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
WOS研究方向 | Engineering ; Instruments & Instrumentation |
WOS类目 | Engineering, Electrical & Electronic ; Instruments & Instrumentation |
WOS记录号 | WOS:000546622100062 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40017 |
专题 | 中科院工业视觉智能装备工程实验室_精密感知与控制 |
通讯作者 | Zhang, Zhengtao |
作者单位 | 1.Chinese Acad Sci CASIA, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Gong, Xinyi,Su, Hu,Xu, De,et al. Visual Defect Inspection for Deep-Aperture Components With Coarse-to-Fine Contour Extraction[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2020,69(6):3262-3274. |
APA | Gong, Xinyi,Su, Hu,Xu, De,Zhang, Jiabin,Zhang, Lei,&Zhang, Zhengtao.(2020).Visual Defect Inspection for Deep-Aperture Components With Coarse-to-Fine Contour Extraction.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,69(6),3262-3274. |
MLA | Gong, Xinyi,et al."Visual Defect Inspection for Deep-Aperture Components With Coarse-to-Fine Contour Extraction".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 69.6(2020):3262-3274. |
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