In view of low accuracy and poor real-time performance of the existing target detection methods, a real-time road target detection method based on depth neural network on the unmanned aerial vehicle(UAV) flight control platform is proposed. The method combines the advantages of YOLOv2 and YOLOv3 networks, and proposes a model of object detection which introduces the Darknet-19 network with residual block and multi-scale features, considering the current situation that YOLOv2 has a low accuracy of road target detection and is difficult to detect small target, and YOLOv3 has a poor real-time performance. The regression classifier is proposed to achieve multi-label classification of overlapping images. The experimental results show that the proposed method has a detection frame rate of 20 frames/s or more on the UAV flight control platform for the video image with a resolution of 416 pixel×416 pixel, the mAP reaches 82.29%, and the recall rate reaches 86.7%, basically meets the requirements of road target detection accuracy and real-time performance on the UAV flight control platform.