Conductive Particle Detection for Chip on Glass Using Convolutional Neural Network
Tao X(陶显)1; Ma WZ(马文治)1; Lu ZF(逯正峰)2; Hou ZX(侯占新)2
Source PublicationIEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
ISSN0018-9456
2021-06
Volume70Issue:1Pages:1-10
Subtype长文
Abstract

The detection of conductive particle images is an important part of the chip on glass (COG) process and can be used to ensure the performance of electrical connections. The segmentation of conductive particles is essential but a difficult task, since the scale and edge of the conductive particles on the chip and the imaging effect are different. In recent years, methods based on deep learning have become the representative method of image segmentation. However, the currently existing methods cannot fully consider the characteristics of conductive particles and have high model complexity. In this article, a multi-frequency feature learning-based convolutional neural network (CNN) is proposed. The entire network structure consists of a basic U-Net module and multi-frequency module (MFM), which are used to enhance multi-frequency feature fusion of conductive particles and accelerate network training. At the same time, for the feature of particle shape, an active contour without edge (ACWE) loss function is designed to extract the fine contour feature of particles. Experimental results on three datasets show the superiority of the proposed method over the major existing mainstream methods with respect to the three performance indicators: recall, precision, and Intersection-over-Union (IoU).

Keyword缺陷检测
MOST Discipline Catalogue工学
DOI10.1109/TIM.2021.3086908
URL查看原文
Indexed BySCIE
Language英语
Sub direction classification目标检测、跟踪与识别
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47201
Collection中科院工业视觉智能装备工程实验室_精密感知与控制
Corresponding AuthorTao X(陶显)
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.China University of Mining and Technology, Beijing
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Tao X,Ma WZ,Lu ZF,et al. Conductive Particle Detection for Chip on Glass Using Convolutional Neural Network[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2021,70(1):1-10.
APA Tao X,Ma WZ,Lu ZF,&Hou ZX.(2021).Conductive Particle Detection for Chip on Glass Using Convolutional Neural Network.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,70(1),1-10.
MLA Tao X,et al."Conductive Particle Detection for Chip on Glass Using Convolutional Neural Network".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 70.1(2021):1-10.
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