Fig. 1. System of real-time road target depth neural network detection for UAV flight control platform

Fig. 2. Model of real-time road target detection based on deep neural network

Fig. 3. Residual block

Fig. 4. Sigmoid function

Fig. 5. Schematic of the predicted bounding box of the 13×13 scale feature map

Fig. 6. Pascal VOC2007, Pascal VOC2012 and self-made VOC data set images by ourselves

Fig. 7. Map of training loss

Fig. 8. Target detection of overlapping images. (a1) and (b1) are the overlapping image detection effect of YOLOv2; (a2) and (b2) are the overlapping image detection effect of our model; (a3) and (b3) are the overlapping image detection effect of YOLOv3

Fig. 9. Target detection of different scenes. (a1) (b1) and (c1) are the object detection effect images of YOLOv2; (a2) (b2) and (c2) are the object detection effect images of our model; (a3) (b3) and (c3) are the object detection effect images of YOLOv3

Fig. 10. NVIDIA Jetson TX2 on real road target inspection. (a1) (b1) and (c1) are the object detection effect images of YOLOv2; (a2) (b2) and (c2) are the object detection effect images of our model; (a3) (b3) and (c3) are the object detection effect images of YOLOv3

Fig. 11. Detection for different targets. (a) Detection for car, bus and person in our model; (b) detection for truck in our model