The uncontrolled spread of forest fires, driven by climate change and increasing human activities, poses a significant global challenge, disrupting the delicate balance of ecosystems. These fires, whether caused by natural disasters or human factors, have severe and often irreversible economic, social, and environmental consequences. The rising frequency of forest fires due to both artificial and natural causes is becoming a major international concern. Fire detection methods range from human observation and satellite-based systems to optical smoke detection and watchtowers. While each method offers advantages depending on the application, traditional approaches often suffer from delayed response times, making early intervention difficult. Unmanned aerial vehicles (UAVs) have emerged as a powerful alternative in fire detection, offering the mobility to access difficult terrain and perform rapid, detailed observations over large forested areas. This study explores how forest fires are detected using UAVs integrated with YOLOv4, YOLOv7, and YOLOv9 models, while also examining the limitations of these algorithms. The performance comparison reveals that YOLOv9 outperforms the other models in terms of precision, recall, and speed. With a precision of 0.922, mAP@50 of 0.915, and mAP@50:95 of 0.872, YOLOv9 demonstrates superior performance, exceeding YOLOv7 by 2.78% and YOLOv4 by 9.33%. These findings offer valuable insights into the use of UAVs for fast and accurate fire detection, highlighting their critical role in combating forest fires effectively. 

Keywords - forest fire, fire detection, deep learning, unmanned aerial vehicle, YOLOv4, YOLOv7, YOLOv9

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