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FireLite: Leveraging Transfer Learning for Efficient Fire Detection in Resource-Constrained Environments
Hasan, Mahamudul Al Hossain Prince, Md Maruf Ansari, Mohammad Samar Jahan, Sabrina Musa Miah, Abu Saleh Shin, Jungpil
Hasan, Mahamudul
Al Hossain Prince, Md Maruf
Ansari, Mohammad Samar
Jahan, Sabrina
Musa Miah, Abu Saleh
Shin, Jungpil
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2024-12-20
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Abstract
Fire hazards are extremely dangerous, particularly in sectors such the transportation industry where political unrest increases the likelihood of their occurring. By employing IP cam eras to facilitate the setup of fire detection systems on transport vehicles losses from fire events may be prevented proactively. However, the development of lightweight fire detection models is required due to the computational constraints of the em bedded systems within these cameras. We introduce ”FireLite,” a low-parameter convolutional neural network (CNN) designed for quick fire detection in contexts with limited resources, in answer to this difficulty. With an accuracy of 98.77%, our model—which has just 34,978 trainable parameters—achieves remarkable performance numbers. It also shows a validation loss of 8.74 and peaks at 98.77 for precision, recall, and F1-score measures. Because of its precision and efficiency, FireLite is a promising.
Citation
Hasan, M., Prince, M. M. A. H., Ansari, M. S., Jahan, S., Miah, A. S. M., & Shin, J. (2024). FireLite: Leveraging Transfer Learning for Efficient Fire Detection in Resource-Constrained Environments. arXiv preprint arXiv:2409.20384.
https://doi.org/10.48550/arXiv.2409.20384
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arXiv (Cornell University)
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Preprint