# FireNet: A Specialized Lightweight Fire & Smoke Detection Model for Real-Time IoT Applications

arXiv: https://arxiv.org/abs/1905.11922 · HTML: https://mlsanity.com/paper/1905.11922/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 14 of 14)

- [A Survey of Modern Deep Learning based Object Detection Models](https://mlsanity.com/paper/2104.11892/) (2021)
- [SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification](https://mlsanity.com/paper/2405.14506/) (2024)
- [DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition](https://mlsanity.com/paper/2410.13663/) (2024)
- [Flexible ViG: Learning the Self-Saliency for Flexible Object Recognition](https://mlsanity.com/paper/2406.18585/) (2024)
- [FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment](https://mlsanity.com/paper/2311.05168/) (2023)
- [Wildfire Smoke Detection System: Model Architecture, Training Mechanism, and Dataset](https://mlsanity.com/paper/2311.10116/) (2023)
- [FireLite: Leveraging Transfer Learning for Efficient Fire Detection in Resource-Constrained Environments](https://mlsanity.com/paper/2409.20384/) (2024)
- [Fire and Smoke Detection with Burning Intensity Representation](https://mlsanity.com/paper/2410.16642/) (2024)
- [UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model](https://mlsanity.com/paper/2501.12087/) (2025)
- [Unified smoke and fire detection in an evolutionary framework with self-supervised progressive data augment](https://mlsanity.com/paper/2202.07954/) (2022)
- [A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition](https://mlsanity.com/paper/2503.22079/) (2025)
- [Uncertainty-Aware Post-Detection Framework for Enhanced Fire and Smoke Detection in Compact Deep Learning Models](https://mlsanity.com/paper/2510.10108/) (2025)
- [Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications](https://mlsanity.com/paper/2605.08196/) (2026)
- [Attention-Based Spatio-Temporal Approach for Fire and Smoke Detection](https://mlsanity.com/paper/2610.20141/) (2026)

## Abstract (arXiv)

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.