Transparency and Traceability for AI-Based Defect Detection in PCB Production

Author:

Rezaei AhmadORCID,Richter JohannesORCID,Nau JohannesORCID,Streitferdt DetlefORCID,Kirchhoff MichaelORCID

Publisher

Springer Nature Switzerland

Reference34 articles.

1. National physical laboratory industry defects database (2022). http://defectsdatabase.npl.co.uk/

2. Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)

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

4. Angelopoulos, A., et al.: Tackling faults in the industry 40 era-a survey of machine-learning solutions and key aspects. Sensors 20(1), 109 (2019)

5. Batista, G.E., Bazzan, A.L., Monard, M.C., et al.: Balancing training data for automated annotation of keywords: a case study. In: WOB, pp. 10–18 (2003)

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