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

• المؤلفون الرئيسيون: Volkau, Ihar, Abdul Mujeeb, Dai, Wenting, Erdt, Marius, Sourin, Alexei
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Manufacturing::Quality control; Defect Detection; Image Analysis; Machine Learning; Transfer Learning; Optical Inspection; Unsupervised Learning
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/154941
• المؤسسة: Nanyang Technological University

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...