FireNet: A Specialized Lightweight Fire & Smoke Detection Model for Real-Time IoT Applications
This work addresses the need for precise, fast, and portable fire detection systems to reduce life and property loss, though it appears incremental as it focuses on optimizing the trade-off between performance and model size.
The paper tackles the problem of fire and smoke detection for real-time IoT applications by proposing FireNet, a lightweight neural network that achieves better performance than existing models while being deployable on embedded platforms like Raspberry Pi, with evaluations on standard and custom datasets showing promising results.
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.