Robust Vehicle Detection in Aerial Images Based on Image Spatial Pyramid Detection Model

Abstract

Vehicle detection in high resolution aerial images obtained by unmanned aerial vehicles (UAV) has a wide application in traffic surveillance. Recently, many detectors based on convolutional neural network (CNN) have achieved great success in object detection. However, it would be difficult for them to perform efficiently on aerial images because the significant difference in target size caused by the altitude change of the UAV platform brings great challenge for these detectors to conduct precise localization. To improve the detection performance on aerial images, we propose an Image Spatial Pyramid Detection Model (ISPDM) which mainly consists of two stages. In the first stage, we divide the image into several patches and select some of them with an image patch selection progress. In the second stage, we utilize YOLOv3 to detect vehicles the original image along with the selected patches and obtain the final result with an integrated decision-making algorithm. Finally, the superiority of the proposed algorithm is well demonstrated by comparison with other solutions for vehicle detection in high resolution aerial images through extensive experiments.