Defect Detection in Printed Circuit Boards Based on EdgeML and Computer Vision
The production of electronic boards is a common activity in the industrial environment, and ensuring their quality is essential for obtaining reliable products. This work presents a comparative study of the performance of three machine learning architectures: YoloV8, FOMO, and MobileNet. The dataset was created based on four classes for assembly fault detection. The model that achieved the best results was FOMO, with precision, recall, and Fl-Score above 95 %, as well as processing 10.5 frames per second and having a total size of 152 kB.
Authors
- Felipe A. P. de Figueiredo (ORCID: https://orcid.org/0000-0002-2167-7286)
- Felipe Ferreira De Oliveira Rocha (ORCID: https://orcid.org/0000-0002-9978-5703)
- Rodrigo de Barros Vimieiro (ORCID: https://orcid.org/0000-0002-0725-6506)
- Hyago V. L. B. Silva
Institutions
- Universidade Federal de São Carlos (BR)
- National Institute of Telecommunications (BR)
Publication Details
- Published
- 2024-12-17
- DOI
- https://doi.org/10.1109/icicyta64807.2024.10913437
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico