FireNet-Lite: A separable convolutional network for ultra-efficient fire image classification

Abstract

Fire outbreaks pose serious threats to ecological systems, human lives, and property, necessitating rapid and efficient detection mechanisms. While traditional fire detection approaches using deep learning have shown promising results, many rely on computationally intensive architectures, limiting their applicability in real-time scenarios and resource-constrained environments. In this study, we propose FireNet-Lite, a lightweight convolutional neural network optimized with depth-wise separable convolutions for fire image classification. FireNet-Lite achieves a test accuracy of 93% and a high recall of 96.57%, using only 7,693 parameters, thereby significantly reducing computational overhead. Owing to its compact size and low latency, the model is well-suited for deployment on edge devices such as surveillance cameras and drones, where efficient and timely fire detection is critical. Experimental results demonstrate that FireNet-Lite effectively balances detection performance with computational efficiency, outperforming many existing models in this regard. Furthermore, the model exhibits robustness to variations in lighting conditions, backgrounds, and flame intensity, enhancing its reliability in diverse real-world environments. Its architecture is specifically designed for low-latency inference, making it highly applicable in real-time fire detection systems. The promising results of FireNet-Lite highlight its potential as a scalable, practical solution for next-generation fire prevention technologies.