SFGI-YOLO: A Multi-Scale Detection Method for Early Forest Fire Smoke Using an Extended Receptive Field

Forest fires pose a significant threat to human life and property. The early detection of smoke and flames can significantly reduce the damage caused by forest fires to human society. This article presents an SFGI-YOLO model based on YOLO11n, which demonstrates outstanding advantages in detecting forest fires and smoke, particularly in the context of early fire monitoring. The main principles of the algorithm include the following: first, a small-object detection head P2 is added to better extract shallow feature information; a Feature Enhancement Module (FEM) is utilized to increase feature richness, expand the receptive field, and enhance detection capabilities for small objects across multiple scales; the lightweight GhostConv is employed to significantly reduce computational costs and decrease the number of parameters; and Inception DWConv is combined with a C3k2 module to utilize multiple parallel branches, thereby enlarging the receptive field. The improved algorithm achieved a mean Average Precision (mAP50) of 95.4% on a custom forest fire dataset, surpassing the YOLO11n model by 1.8%. This model offers more accurate detection of forest fires, reducing both missed detections and false positives and thereby meeting the high precision and real-time detection requirements in forest fire monitoring.