NDLI: Improved bare PCB defect detection approach based on deep feature learning

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Improved bare PCB defect detection approach based on deep feature learning

Content Provider	IET Digital Library
Author	Zhang, Can Shi, Wei Li, Xiaofei Zhang, Haijian Liu, Hong
Abstract	Robust and precise defect detection is of great significance in the production of the high-quality printed circuit board (PCB). However, due to the complexity of PCB production environments, most previous works still utilise traditional image processing and matching algorithms to detect PCB defects. In this work, an improved bare PCB defect detection approach is proposed by learning deep discriminative features, which also greatly reduced the high requirement of a large dataset for the deep learning method. First, the authors extend an existing PCB defect dataset with some artificial defect data and affine transformations to increase the quantity and diversity of defect data. Then, a deep pre-trained convolutional neural network is employed to learn high-level discriminative features of defects. They fine-tune the base model on the extended dataset by freezing all the convolutional layers and training the top layers. Finally, the sliding window approach is adopted to further localise the defects. Extensive comparisons with three traditional shallow feature-based methods demonstrate that the proposed approach is more feasible and effective in PCB defect detection area.
Starting Page	1415
Ending Page	1420
Page Count	6
Volume Number	2018
e-ISSN	20513305
Issue Number	Issue 16, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2018/16
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8275
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2018-08-16
Access Restriction	Open
Rights License	Creative Commons Attribution-Non Commercial-No Derivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Subject Keyword	Affine Transformations Affine Transforms Artificial Defect Data Automatic Optical Inspection Computer Vision And Image Processing Technique Convolution Convolutional Layer Convolutional Neural Network Deep Discriminative Features Deep Feature Learning Electronic Engineering Computing Feature Extraction Feedforward Neural Network High-level Discriminative Feature High-quality Printed Circuit Board Image Matching Image Processing Image Representation Inspection And Quality Control Integral Transforms Integral Transforms in Numerical Analysis Learning in AI Matching Algorithm Neural Computing Technique Optical, Image And Video Signal Processing PCB Defect Detection Approach PCB Production Environments Printed Circuit Printed Circuit Manufacture Shallow Feature-based Method Sliding Window Approach
Content Type	Text
Resource Type	Article

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