FDN: A Real-Time Ensemble Fire Detection Network

Fires frequently occur worldwide, devastating neighborhoods and forests owing to challenges in early extinguishment. Computer vision-based deep learning models have recently been employed in fire detection research. The primary objective of this study is to develop a multiclass fire detection model using images and videos. In this study, an ensemble fire detection model, FireDetectNet (FDN), comprising several classification models (ResNeXt-50 ( $32\times 4$ d), EfficinetNet-B4) and one object detection model (Yolov5) is proposed. This model classifies fire and three different colors of smoke, fog, light, and sunlight. The training of multiple as an ensemble results in their performance being superior to that of a single classification or detection model. In the classification phase, our model (FDN) achieves an F1-score that is 11.3% higher than that of the single-classification model and 31.7% higher specifically for gray smoke. In the detection phase, mAP@50 and mAP@50:95 are improved by 1.3% and 1.6%, respectively, compared to the single detection model, Yolov5s. The proposed model effectively distinguishes between fog and clouds, similar to smoke, and light and sunlight, akin to a real fire using the Partial-Loss-Balanced Task Weighting (P-LBTW) algorithm we develop to reduce negative transfer by partially learning task-specific weights. Leveraging the classified and detected results, wildfires can be promptly and accurately identified wherever the equipment is deployed, thus mitigating the risk of large-scale fires. Therefore, the proposed real-time ensemble fire detection system offers practical models and system configurations for field deployment, facilitating early fire suppression to mitigate fire damage.