An adaptive incremental two-stage framework for crack defect detection
Author:
Funder
Key Project of National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-18133-y.pdf
Reference43 articles.
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2. Singh SA, Desai KA (2022) Automated surface defect detection framework using machine vision and convolutional neural networks. J Intell Manuf 34(4):1995–2011. https://doi.org/10.1007/s10845-021-01878-w
3. Cheng L, Yi J, Chen A et al (2023) Fabric defect detection based on separate convolutional UNet. Multimed Tools Appl 82(2):3101–3122. https://doi.org/10.1007/s11042-022-13568-7
4. Shang H, Sun C, Liu J et al (2023) Defect-aware transformer network for intelligent visual surface defect detection. Adv Eng Inf 55:101882. https://doi.org/10.1016/j.aei.2023.101882
5. Zhang H, Pan R, Chang F et al (2023) Zero-DD: Zero-sample defect detection for industrial products. Comput Electr Eng 105:108516. https://doi.org/10.1016/j.compeleceng.2022.108516
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