Detecting defects in PCB manufacturing: an exploration using Yolov8 deep learning
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12008-024-01986-w.pdf
Reference17 articles.
1. Jeon, M., Yoo, S., Kim, S.-W.: A contactless PCBA defect detection method: Convolutional neural networks with thermographic images. IEEE Trans. Compon. Packag. Manuf. Technol. 12(3), 489–501 (2022)
2. Alghassab, M.A.: Defect detection in with pre-trained feature extraction methodology with convolution neural networks. Computers Mater. Continua. 70(1), 637–652 (2022)
3. Nguyen, V.-T., Bui, H.-A.: A real-time defect detection in applying deep learning. EUREKA: Phys. Eng. 2, 143–153 (2022)
4. An, K., Zhang, Y.: LPViT: A transformer based model for PCB image classification and defect detection. IEEE Access. 10, 42542–42553 (2022)
5. Bhattacharya, A., Cloutier, S.G.: End-to-end deep learning framework for printed circuit board manufacturing defect classification. Sci. Rep. 12(1), 12559 (2022)
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