Lightweight PCB defect detection algorithm based on MSD-YOLO
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Link
https://link.springer.com/content/pdf/10.1007/s10586-023-04156-x.pdf
Reference24 articles.
1. Zhang, H., Jiang, L., Li, C.: CS-ResNet: cost-sensitive residual convolutional neural network for PCB cosmetic defect detection. Expert Syst. Appl. 185, 115673 (2021). https://doi.org/10.1016/j.eswa.2021.115673
2. Annaby, M.H., Fouda, Y.M., Rushdi, M.A.: Improved normalized cross-correlation for defect detection in printed-circuit boards. IEEE Trans. Semicond. Manuf. 32(2), 199–211 (2019). https://doi.org/10.1109/TSM.2019.2911062
3. Gaidhane, V.H., Hote, Y.V., Singh, V.: An efficient similarity measure approach for PCB surface defect detection. Pattern Anal. Appl. 21, 277–289 (2018). https://doi.org/10.1007/s10044-017-0640-9
4. Deng, Y.-S., Luo, A.-C., Dai, M.-J.: Building an automatic defect verification system using deep neural network for PCB defect classification. In: 4th International Conference on Frontiers of Signal Processing (ICFSP), Poitiers, France, Sep 24–27 (2018)
5. Luo, J., Yang, Z., Li, S., Wu, Y.: FPCB surface defect detection: a decoupled two-stage object detection framework. IEEE Trans. Instrum. Meas. 1–11, 277–289 (2021). https://doi.org/10.1109/TIM.2021.3092510
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