This study proposes a method of landing runway detection based on YOLOv5 network architecture to solve the critical problem of fast and robust runway detection for engineering applications of UAV autonomous landing technology. First, the captured airborne front-view images were enhanced to improve the robustness of the network model based on the YOLOv5 network architecture. Then, features with different scales and different dimensions were fused to improve the precision of the detection network model. Furthermore, the geometric features of the runway were incorporated into the loss function design in the prediction layer to optimize the prediction model. In this study, AirSim was used to simulate visual image landing datasets under complex conditions to validate the effectiveness of the proposed method. The simulation results on these datasets show that the average detection speed of the runway detection algorithm proposed in this study can reach 125 frame/s, and the average detection accuracy is 99%, which outperforms other traditional methods and can meet the fast and accurate requirements of runway detection.