Efficient UAV-Based Forest Fire Detection Using CNN and YOLOv8 Integration

Wild forest fires pose a significant threat to ecosystems, causing habitat destruction, biodiversity loss, and severe air pollution. The rapid spread of uncontrolled fires leads to irreversible environmental degradation and increased greenhouse gas emissions. Timely and accurate fire detection is therefore essential to minimize risks to both human life and economic assets. This paper presents a UAV-based wildfire detection framework that integrates Convolutional Neural Networks (CNNs) for image classification with YOLOv8 for real-time object localization. The system was trained on 10,000 UAV-acquired images and achieved a classification accuracy of 92.6%, with an F1-score of 0.93, precision of 0.95, and recall of 0.92. The YOLOv8 model demonstrated strong detection capabilities, achieving a mean Average Precision (mAP) of 44.5% and an image processing time of 0.75 seconds per frame. Comparative evaluations reveal a 15% improvement in accuracy and a 20% reduction in computational cost over traditional methods. The proposed framework also exhibits robustness to noise and compression artifacts, attaining a Normalized Cross-Correlation (NCC) of 0.98 under Gaussian noise and a Peak Signal-to-Noise Ratio (PSNR) of 38.4 dB under compression. These results highlight the system’s potential for effective real-time UAV-based wildfire surveillance, delivering reliable detection and rapid response in challenging environments.