Abstract: To solve the problem that the existing methods were not good at detecting vehicles on ground roads, a vehicle detection method was proposed based on deep learning and high-resolution remote sensing images. The interested regions of high-resolution remote sensing images were extracted, and the multi-object detection of vehicles was performed by the improved YOLOv3 model, which utilized multi-scale features for object detection. The training process was determined and carried out on the high-resolution remote sensing image dataset, and the image recognition and detection tests were performed with accuracy rate, recall rate and F value as evaluation indicators. The results show that using the proposed method to analyze the high-resolution remote sensing images, the accuracy rate, recall rate and F value are 98.01%, 97.23% and 97.57%, respectively, and the traffic flow per second can be obtained according to the statistics of the detection results. The proposed method can be used as effective supplementary method for monitoring the distribution of information on ground vehicles.