A novel approach based on convolutional neural networks ensemble for fire detection
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03508-3.pdf
Reference47 articles.
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2. Ghali, R., Akhloufi, M.A., Mseddi, W.S.: Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation. Sensors, 22(5), 1977. (2022). https://doi.org/10.3390/s22051977
3. Valikhujaev, Y., Abdusalomov, A., Cho, Y.I.: Automatic fire and Smoke Detection Method for Surveillance Systems based on dilated CNNs. Atmosphere. 11(11), 1241 (2020). https://doi.org/10.3390/atmos11111241
4. Yar, H., Khan, Z.A., Hussain, T., Baik, S.W.: A modified vision transformer architecture with scratch learning capabilities for effective fire detection. Expert Syst. Appl. 252, 123935 (2024). https://doi.org/10.1016/j.eswa.2024.123935
5. Çelik, T., Demirel, H.: Fire detection in video sequences using a generic color model. Fire Saf. J. 44(2), 147–158 (2009). https://doi.org/10.1016/j.firesaf.2008.05.005
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