Convolutional neural network‐based multi‐label classification of PCB defects-Reference-Cited by-同舟云学术

Convolutional neural network‐based multi‐label classification of PCB defects

Published:2018-10-22 Issue:16 Volume:2018 Page:1612-1616
ISSN:2051-3305
Container-title:The Journal of Engineering
language:en
Short-container-title:J. eng.

Author:

Zhang Linlin¹,Jin Yongqing²,Yang Xuesong³,Li Xia²,Duan Xiaodong⁴,Sun Yuan¹,Liu Hong²

Affiliation:

1. College of Information EngineeringMinzu University of ChinaBeijingPeople's Republic of China

2. Key Laboratory of Machine PerceptionPeking University, Shenzhen Graduate SchoolPeople's Republic of China

3. University of Illinois Urbana‐ChampaignUSA

4. Department of Electronic SystemsAalborg UniversityDenmark

Publisher

Institution of Engineering and Technology (IET)

Subject

General Engineering,Energy Engineering and Power Technology,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/joe.2018.8279

Reference27 articles.

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3. A novel algorithm for inspecting and classifying the defects of printed circuit boards;Chen X.J.;J. Huazhong Univ. Sci. Technol. Med. Sci.,2010

4. PuteraS.H.I. IbrahimZ.: ‘Printed circuit board defect detection using mathematical morphology and MATLAB image processing tools’.Int. Conf. on Education Technology and Computer Shanghai 2010 pp.V5‐359–V5‐363

5. PuteraS.H.I. DzafaruddinS.F. MohamadM.: ‘MATLAB based defect detection and classification of printed circuit board’.Int. Conf. on Digital Information and Communication Technology and it's Applications (DICTAP) Bangkok 2012 pp.115–119

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