A Highly Efficient and Lightweight Detection Method for Steel Surface Defect
Author:
Funder
Tianjin Project+Team Key Training Special Project
Science and Technology Support of Tianjin Key Research and the Development Plan Project
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10921-024-01084-7.pdf
Reference43 articles.
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2. Lian, J., Jia, W., Zareapoor, M., Zheng, Y., Luo, R., Jain, D.K., Kumar, N.: Deep-learning-based small surface defect detection via an exaggerated local variation-based generative adversarial network. IEEE Trans. Ind. Inform. 16(2), 1343–1351 (2019). https://doi.org/10.1109/TII.2019.2945403
3. Li, W., Qi, J., Sun, H.: Taf2-net: triple-branch attentive feature fusion network for ultrasonic flaw detection. IEEE Trans. Instrum. Meas. 71, 1–12 (2022). https://doi.org/10.1109/TIM.2022.3150592
4. Shu, Y.F., Li, B., Li, X., Xiong, C., Cao, S., Wen, X.Y.: Deep learning-based fast recognition of commutator surface defects. Measurement 178, 109324 (2021). https://doi.org/10.1016/j.measurement.2021.109324
5. Xu, Y., Li, D., Xie, Q., Wu, Q., Wang, J.: Automatic defect detection and segmentation of tunnel surface using modified mask r-cnn. Measurement 178, 109316 (2021). https://doi.org/10.1016/j.measurement.2021.109316
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