Open AccessResearch on automatic defect identification technology of electronic components
Author(s) -
Yin Liu,
Kui Zhang,
Cui Yaru,
An Shengbiao,
Jie Huang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012210
Subject(s) - computer science , identification (biology) , electronic component , artificial intelligence , deep learning , quality (philosophy) , electronic systems , component (thermodynamics) , pattern recognition (psychology) , electronic engineering , electrical engineering , engineering , philosophy , botany , epistemology , biology , physics , thermodynamics
Aiming at the problems of low efficiency and low accuracy caused by manual defect detection of electronic components, FCN, SegNet/DeconvNet and DeepLab and other deep learning technologies are studied. Using Caffe, Keras, PyTorch and other frameworks, an automatic defect identification system for electronic components is developed to distinguish qualified products from unqualified ones, so as to realize intelligent defect detection of electronic components. At the same time, the detection accuracy rate is greatly improved, and the quality of electronic components is guaranteed.