Real-time aerial fire detection on resource-constrained devices using knowledge distillation

• Published in: International journal of applied earth observation and geoinformation Vol. 142; p. 104665
• Main Authors: Jangirova, Sabina, Jankovic, Branislava, Ullah, Waseem, Khan, Latif U., Guizani, Mohsen
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2025; Elsevier
• Subjects: Aerial images; Convolution neural network; Fire detection; Knowledge distillation; Vision transformer; Wildfires; Vision transformer; Aerial images; Wildfires; Knowledge distillation; Convolution neural network; Fire detection
• ISSN: 1569-8432
• DOI: 10.1016/j.jag.2025.104665

Wildfire catastrophes cause significant environmental degradation, human losses, and financial damage. To mitigate these severe impacts, early fire detection and warning systems are crucial. Current systems rely primarily on fixed CCTV cameras with a limited field of view, restricting their effectiveness in large outdoor environments. The fusion of intelligent fire detection with remote sensing improves coverage and mobility, enabling monitoring in remote and challenging areas. Existing approaches predominantly utilize convolutional neural networks and vision transformer models. While these architectures provide high accuracy in fire detection, their computational complexity limits real-time performance on edge devices such as UAVs. In our work, we present a lightweight fire detection model based on MobileViT-S, compressed through the distillation of knowledge from a stronger teacher model. The ablation study highlights the impact of a teacher model and the chosen distillation technique on the model’s performance improvement. We generate activation map visualizations using Grad-CAM to confirm the model’s ability to focus on relevant fire regions. The high accuracy and efficiency of the proposed model make it well-suited for deployment on satellites, UAVs, and IoT devices for effective fire detection. Experiments on common fire benchmarks demonstrate that our model surpasses the state-of-the-art model by 0.44%, 2.00% while maintaining a compact model size. Our model delivers the highest processing speed among existing works, achieving real-time performance on resource-constrained devices. •We develop UAVs based light-weight model with explainable AI for wildfire.•Employ knowledge distillation to transfer larger model knowledge to small models.•Performed ablation study to evaluate teacher and student models performance.•Proposed model is evaluated three benchmarks’ datasets and achieved SOTA accuracy.