FireLite: Leveraging Transfer Learning for Efficient Fire Detection in Resource-Constrained Environments

TL;DR

FireLite is a lightweight, high-accuracy CNN model designed for rapid fire detection in resource-limited environments, such as transport vehicles with embedded IP cameras, leveraging transfer learning for efficiency.

Contribution

The paper introduces FireLite, a novel low-parameter CNN model that achieves high accuracy and efficiency for fire detection in resource-constrained settings.

Findings

Achieves 98.77% accuracy with only 34,978 parameters.

Demonstrates high precision, recall, and F1-score in fire detection.

Shows potential for deployment in embedded systems with limited computational resources.

Abstract

Fire hazards are extremely dangerous, particularly in sectors such as the transportation industry, where political unrest increases the likelihood of their occurrence. By employing IP cameras 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 embedded 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 response 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 solution for fire detection in resource-constrained environments.