Fine-Tuned SqueezeNet Lightweight Model for Classifying Surface Defects in Hot-Rolled Steel
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-43085-5_18
Reference19 articles.
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2. Ahmed, K.R.: DSTEELNET: a real-time parallel dilated CNN with atrous spatial pyramid pooling for detecting and classifying defects in surface steel strips. Sensors 23(1), 544 (2023). https://doi.org/10.3390/s23010544
3. Fu, G., et al.: A deep-learning-based approach for fast and robust steel surface defects classification. Opt. Lasers Eng. 121, 397–405 (2019). https://doi.org/10.1016/j.optlaseng.2019.05.005
4. Gómez-Sirvent, J.L., López de la Rosa, F., Sánchez-Reolid, R., Morales, R., Fernández-Caballero, A.: Defect classification on semiconductor wafers using fisher vector and visual vocabularies coding. Measurement 202, 111872 (2022). https://doi.org/10.1016/j.measurement.2022.111872
5. Gómez-Sirvent, J.L., de la Rosa, F.L., Sánchez-Reolid, R., Fernández-Caballero, A., Morales, R.: Optimal feature selection for defect classification in semiconductor wafers. IEEE Trans. Semicond. Manuf. 35(2), 324–331 (2022). https://doi.org/10.1109/TSM.2022.3146849
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