NDLI: HRIPCB: a challenging dataset for PCB defects detection and classification

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HRIPCB: a challenging dataset for PCB defects detection and classification

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HRIPCB: a challenging dataset for PCB defects detection and classification

Content Provider	IET Digital Library
Author	Huang, Weibo Wei, Peng Zhang, Manhua Liu, Hong
Abstract	To cope with the difficulties in inspection and classification of defects in printed circuit board (PCB), many methods have been proposed in previous work. However, few of them publish their datasets before, which hinders the introduction and comparison of new methods. In this study, HRIPCB, a synthesised PCB dataset that contains 1386 images with 6 kinds of defects is proposed for the use of detection, classification and registration tasks. Besides, a reference-based method is adopted to inspect and an end-to-end convolutional neural network is trained to classify the defects, which are collectively referred to as the RBCNN approach. Unlike conventional approaches that require pixel-by-pixel processing, the RBCNN method proposed in this study firstly locates the defects and then classifies them by deep neural networks, which shows superior performance on the dataset.
Starting Page	303
Ending Page	309
Page Count	7
Volume Number	2020
e-ISSN	20513305
Issue Number	Issue 13, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2020/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1183
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-01-13
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	6 Kinds Challenging Dataset Classification Computer Vision Computer Vision And Image Processing Technique Deep Neural Network End-to-end Convolutional Neural Network HRIPCB Image Recognition Image Registration Inspection Neural Computing Technique Neural Nets Object Detection Optical, Image And Video Signal Processing PCB Defects Detection Pixel-by-pixel Processing Printed Circuit Printed Circuit Board RBCNN Approach RBCNN Method Reference-based Method Registration Task Synthesised PCB Dataset
Content Type	Text
Resource Type	Article

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