FedDFA: Dual-Factor Aggregation for Federated Driver Distraction Detection
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-7532-5_15
Reference18 articles.
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3. Cronje, J., Engelbrecht, A.P.: Training convolutional neural networks with class based data augmentation for detecting distracted drivers. In: Proceedings of the 9th International Conference on Computer and Automation Engineering, pp. 126–130 (2017)
4. Eraqi, H.M., Abouelnaga, Y., Saad, M.H., Moustafa, M.N.: Driver distraction identification with an ensemble of convolutional neural networks. vol. 2019. Hindawi (2019)
5. Flores-Monroy, J., Nakano-Miyatake, M., Perez-Meana, H., Escamilla-Hernandez, E., Sanchez-Perez, G.: A CNN-based driver’s drowsiness and distraction detection system. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds.) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science Mexican Conference on Pattern Recognition, vol. 13264, pp. 83–93. Springer (2022). https://doi.org/10.1007/978-3-031-07750-0_8
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