The impact of a number of samples on unsupervised feature extraction, based on deep learning for detection defects in printed circuit boards

The impact of a number of samples on unsupervised feature extraction, based on deep learning for detection defects in printed circuit boards

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Deep learning provides new ways for defect detection in automatic optical inspections (AOI). However, the existing deep learning methods require thousands of images of defects to be used for training the algorithms. It limits the usability of these approaches in manufacturing, due to lack of images...

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Bibliographic Details
Main Authors: Volkau, Ihar, Abdul Mujeeb, Dai, Wenting, Erdt, Marius, Sourin, Alexei
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Engineering::Manufacturing::Quality control
Defect Detection
Image Analysis
Machine Learning
Transfer Learning
Optical Inspection
Unsupervised Learning
Online Access:https://hdl.handle.net/10356/154941

Internet

https://hdl.handle.net/10356/154941

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