YOLO-UFS: A Novel Detection Model for UAVs to Detect Early Forest Fires

Forest fires pose a major threat to ecosystems and human life; Therefore, early detec-tion is essential for effective prevention. Traditional detection methods often fall short of the need due to their large coverage and limitations in providing timely alerts. Alt-hough advances in drone technology and deep learning have opened up new possibili-ties for efficient and accurate forest fire detection, implementation rates remain low due to the complexity of deep learning algorithms. This study explores the application of small UAVs equipped with lightweight deep learning models for early forest fire detection. A high-quality dataset was constructed through aerial image analysis, which provided strong support for model training. Based on YOLOv5s, a YOLO-UFS (YOLO-UAVs for Fire and Smoke Detection) network is proposed, which combines enhancements such as C3-MNV4 module, BiFPN, new AF-lou loss function, anchorless detector and NAM attention mechanism. These modifications resulted in the model achieving 91.3% mAP under the same experimental conditions and using a self-built early forest fire dataset. Compared to the original model, the YOLO-UFS model im-proved accuracy, recall, and average accuracy by 3.8%, 4.1%, and 3.2%, respectively, while reducing floating-point arithmetic and parameter counting by 74.7% and 78.3%. Compared with other mainstream YOLO series algorithms, its performance on the UAV platform is superior, effectively balancing accuracy and real-time. In the later stages of the forest fire, using a public dataset, mAP0.5 increased from 85.2% to 86.3%, and mAP0.5:0.95 increased from 56.7% to 57.9%, resulting in an overall mAP increase of 3.3 percentage points. The optimized model demonstrates significant detection ad-vantages in the complex environment captured by small UAVs. This study uses air-borne visible images to provide effective data and methodological support for the early extinguishing of forest fires, which is helpful to achieve the "three early" goals of forest fire prevention (early detection, early mobilization, and early extinguishment). Future work will focus on exploring multi-sensor data capabilities to further improve the ac-curacy and reliability of detection.