Industry-Oriented Detection Method of PCBA Defects Using Semantic Segmentation Models-Reference-Cited by-同舟云学术

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Industry-Oriented Detection Method of PCBA Defects Using Semantic Segmentation Models

Published:2024-06 Issue:6 Volume:11 Page:1438-1446
ISSN:2329-9266
Container-title:IEEE/CAA Journal of Automatica Sinica
language:
Short-container-title:IEEE/CAA J. Autom. Sinica

Author:

Li Yang¹^ORCID,Wang Xiao¹^ORCID,He Zhifan²^ORCID,Wang Ze²^ORCID,Cheng Ke²^ORCID,Ding Sanchuan²^ORCID,Fan Yijing¹^ORCID,Li Xiaotao¹^ORCID,Niu Yawen¹^ORCID,Xiao Shanpeng¹^ORCID,Hao Zhenqi²^ORCID,Gao Bin²^ORCID,Wu Huaqiang²^ORCID

Affiliation:

1. China Mobile Research Institute,Department of Internet of Things Technology and Application,Beijing,China,100053

2. Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuit, Tsinghua University,Beijing,China,100084

Funder

Tsinghua University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6570654/10539265/10539290.pdf?arnumber=10539290

Reference26 articles.

1. Application of Neural Networks in Optical Inspection and Classification of Solder Joints in Surface Mount Technology

2. PCB inspection for missing or misaligned components using background subtraction;Sundaraj;WSEAS Trans. Inform. Sci. Appl.,2009

3. Defect Classification of Electronic Circuit Board Using SVM based on Random Sampling

4. TDD‐net: a tiny defect detection network for printed circuit boards

5. Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network

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