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

• Main Authors: Volkau, Ihar, Abdul Mujeeb, Dai, Wenting, Erdt, Marius, Sourin, Alexei
• 其他作者: School of Electrical and Electronic Engineering
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Engineering::Manufacturing::Quality control; Defect Detection; Image Analysis; Machine Learning; Transfer Learning; Optical Inspection; Unsupervised Learning
• 在線閱讀: https://hdl.handle.net/10356/154941

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...