Rapid Fire Detection with Early Exiting
Abstract
Efficient and effective fire detection has proven critical and if not achieved it can pose significant ecological and economic challenges. By introducing early exits into fire video processing using MSDNet, our approach enables quick identification of fires and smoke, ensuring a prompt response to potential fire incidents. Emphasizing efficiency, our method is tailored for resource-constrained edge devices, providing a practical solution for fire-prone regions and enhancing overall fire detection and prevention efforts. Investigating different model sizes yielded accuracy ranging from 86% to 94%, with smaller models outperforming larger models. The adoption of MSDNet allowed for the achievement of an F1-Score of 0.2. This preliminary work has shown the value of small models in the robust detection of fires and introducing early exits can further performance.