Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke

Abstract

Accurate forest fire detection is critical for the timely intervention and mitigation of environmental disasters. It is very important to intervene in forest fires before major damage occurs by using smoke data. This study proposes a novel deep learning-based approach that significantly enhances the accuracy of fire detection by incorporating advanced feature extraction techniques. Through rigorous experiments and comprehensive evaluations, our method outperforms existing approaches, demonstrating its effectiveness in detecting fires at an early stage. The proposed approach leverages convolutional neural networks to automatically identify fire signatures from remote sensing images, offering a reliable and efficient solution for forest fire monitoring. A total of 30 different object detection models, including the proposed model, were run with the extended Wildfire Smoke dataset, and the results were compared. As a result of extensive experiments, it was observed that the proposed model gave the best result among all models, with a test mAP value of 96.9%. Our findings not only contribute to the advancement of fire detection technologies, but also underscore the importance of deep learning in addressing real-world environmental challenges.