A Survey of PCB Defect Detection Algorithms-Reference-Cited by-同舟云学术

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A Survey of PCB Defect Detection Algorithms

Published:2023-12 Issue:5-6 Volume:39 Page:541-554
ISSN:0923-8174
Container-title:Journal of Electronic Testing
language:en
Short-container-title:J Electron Test

Author:

Lakshmi Gayathri,Sankar V. Udaya^ORCID,Sankar Y. Siva

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering

Link

https://link.springer.com/content/pdf/10.1007/s10836-023-06091-6.pdf

Reference51 articles.

1. Adibhatla VA, Chih H-C, Hsu C-C, Cheng J, Abbod MF, Shieh J-S (2020) Defect detection in printed circuit boards using you-only-look-once convolutional neural networks. Electronics 9(9):1547

2. Adibhatla VA, Shieh J-S, Abbod MF, Chih H-C, Hsu C-C, Cheng J (2018) Detecting defects in PCB using deep learning via convolution neural networks. In: 2018 Proc. 13th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT). IEEE, pp 202–205

3. Anitha D, Rao M (2019) SMT Component Inspection in PCBA’s using image processing techniques. International Journal of Innovative Technology and Exploring Engineering (IJITEE) 8(12):541–547

4. Bonello D, Iano Y, Neto U (2020) A novel approach to the PCBA’s defects detection using background algorithm. Int J Electr Comput Eng 8:308–315

5. Chaudhary V, Dave IR, Upla KP (2017) Automatic visual inspection of printed circuit board for defect detection and classification. In: Proc. 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, pp 732–737

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3. Literature Review: Global Criticality Assessment Based on Feature Surrogates at the PCBA Levels;2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE);2024-04-07

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