A real-time and efficient surface defect detection method based on YOLOv4
Author:
Publisher
Springer Science and Business Media LLC
Subject
Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s11554-023-01333-4.pdf
Reference38 articles.
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2. Borselli, A., Colla, V., Vannucci, M.: Surface defects classification in steel products: a comparison between different artificial intelligence-based approaches. In: Artificial intelligence and applications/718: modelling, identification, and control. ACTAPRESS, Innsbruck, Austria (2011)
3. Anter, A.M., Hassenian, A.E., Oliva, D.: An improved fast fuzzy c-means using crow search optimization algorithm for crop identification in agricultural. Expert Syst. Appl. 118, 340–354 (2019). https://doi.org/10.1016/j.eswa.2018.10.009
4. Luo, J., Yang, Z., Li, S., Wu, Y.: FPCB surface defect detection: a decoupled two-stage object detection framework. IEEE Trans. Instrum. Meas. 70, 1–11 (2021). https://doi.org/10.1109/TIM.2021.3092510
5. Kang, Z., Yuan, C., Yang, Q.: The fabric defect detection technology based on wavelet transform and neural network convergence. In: 2013 IEEE international conference on information and automation (ICIA). pp. 597–601. IEEE, Yinchuan, China (2013)
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