Steel surface defect recognition using classifier combination
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00170-024-13407-z.pdf
Reference52 articles.
1. Tian Y, Zhang T, Zhang Q, Li Y, Wang Z (2020) Feature fusion-based preprocessing for steel plate surface defect recognition. Math Biosci Eng 17:5672–5685. https://doi.org/10.3934/mbe.2020305
2. Luo Q, Fang X, Liu L, Yang C, Sun Y (2020) Automated visual defect detection for flat steel surface: a survey. IEEE Trans Instrum Meas 69(3):626–644. https://doi.org/10.1109/TIM.2019.2963555
3. Bouguettaya A, Mentouri Z, Zarzour H (2023) Deep ensemble transfer learning-based approach for classifying hot-rolled steel strips surface defects. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-023-10947-8
4. Wan X, Zhang X, Liu L (2021) An improved VGG19 transfer learning strip steel surface defect recognition deep neural network based on few samples and imbalanced datasets. Appl Sci 11(6). https://doi.org/10.3390/app11062606
5. Feng X, Gao X, Luo L (2021) A ResNet50-based method for classifying surface defects in hot-rolled strip steel. Mathematics 9(19). https://doi.org/10.3390/math9192359
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