Fire disasters typically result in lot of loss to life and property. It is therefore imperative that precise, fast, and possibly portable solutions to detect fire be made readily available to the masses at reasonable prices. There have been several research attempts to design effective and appropriately priced fire detection systems with varying degrees of success. However, most of them demonstrate a trade-off between performance and model size (which decides the model's ability to be installed on portable devices). The work presented in this paper is an attempt to deal with both the performance and model size issues in one design. Toward that end, a `designed-from-scratch' neural network, named FireNet, is proposed which is worthy on both the counts: (i) it has better performance than existing counterparts, and (ii) it is lightweight enough to be deploy-able on embedded platforms like Raspberry Pi. Performance evaluations on a standard dataset, as well as our own newly introduced custom-compiled fire dataset, are extremely encouraging.
@article{arxiv.1905.11922,
title = {FireNet: A Specialized Lightweight Fire & Smoke Detection Model for Real-Time IoT Applications},
author = {Arpit Jadon and Mohd. Omama and Akshay Varshney and Mohammad Samar Ansari and Rishabh Sharma},
journal= {arXiv preprint arXiv:1905.11922},
year = {2019}
}