HRIPCB: a challenging dataset for PCB defects detection and classification
Author:
Affiliation:
1. The Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking UniversityShenzhenPeople's Republic of China
2. Shenzhen Skyworth‐RGB Electronic Co., Ltd.ShenzhenPeople's Republic of China
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.2019.1183
Reference22 articles.
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2. Detection of PCB based on digital image processing;Zheng‐Ming L.I.;Instrum. Techn. Sens.,2012
3. ChaudharyV. DaveI.R. UplaK.P.: ‘Automatic visual inspection of printed circuit board for defect detection and classification’.Proc. of the 2017 Int. Conf. on Wireless Communications Signal Processing and Networking Chennai India 2017 pp.732–737
4. KumarS. IwahoriY. BhuyanM.K.: ‘PCB defect classification using logical combination of segmented copper and non‐copper part’.Proc. of Int. Conf. on Computer Vision and Image Processing Roorkee India 2017 pp.523–532
5. Neural network paradigm for classification of defects on PCB;Heriansyah R.;Jurnal Teknologi,2003
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