Efficient Aerial Fire Detection on Resource-Constrained Devices Using Cross-Architecture Knowledge Distillation

Fire poses significant ecological danger and can lead to human losses if not mitigated early. Fire detection systems on remote sensing devices have become critical to disaster prevention and management. In this paper, we propose a lightweight and effective fire detection model based on MobileViT-XS, compressed through the distillation of knowledge from a stronger teacher model. Our model combines the local feature extraction capabilities of convolutional networks with the global context capabilities of vision transforms. This allows our proposed model to achieve high detection accuracy and real-time inference, making it suitable for deployment on UAVs and other resource-constrained devices. Through rigorous experiments on the ADSF dataset, the proposed method achieves an accuracy, F1-score, and recall of 95.50%, along with a precision of 95.52%, thereby outperforming contemporary methods. Extensive evaluations on ADSF confirm that our method not only surpasses existing state-of-the-art techniques in accuracy but also provides significantly faster processing speeds.