Author:
Momot A., ,Kretsul V., ,Muraviov O., ,Galagan R.,
Publisher
International Association Welding
Reference15 articles.
1. 1. Steshenko, Y., Momot A., Protasov A., Muraviov O. (2023) Automation of the process of segmentation of images of metal surface defects using the neural network U-Net. Tekh. Diahnost. ta Neruiniv. Kontrol, 2, 34-40 [in Ukrainian]. DOI: https://doi.org/10.37434/tdnk2023.02.05
2. 2. Ling, Q., Isa, N.A. (2023) Printed circuit board defect detection methods based on image processing, machine learning and deep learning: A survey. IEEE Access, 11, 15921-15944 [in English]. DOI: https://doi.org/10.1109/access.2023.3245093
3. 3. Khandpur, R.S. (2006) Printed circuit boards: Design, fabrication, assembly and testing. New York, McGraw-Hill.
4. 4. Zhou, Y., Yuan M., Zhang, J. et al. (2023) Review of vision- based defect detection research and its perspectives for printed circuit board. J. of Manufacturing Systems, 70, 557- 578. DOI: https://doi.org/10.1016/j.jmsy.2023.08.019
5. 5. Akhatova, A. (2021) PCB defects, Kaggle. https://www.kaggle. com/datasets/akhatova/pcb-defects