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: Ihar Volkau, Abdul Mujeeb, Wenting Dai, Marius Erdt, Alexei Sourin
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Future Internet
Subjects:
defect detection
image analysis
machine learning
transfer learning
automatic optical inspection
unsupervised learning
Online Access:https://www.mdpi.com/1999-5903/14/1/8

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https://www.mdpi.com/1999-5903/14/1/8

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