Rapid Vehicle Detection in Aerial Images under the Complex Background of Dense Urban Areas

Abstract

Vehicle detection on aerial remote sensing images under the complex background of urban areas has always received great attention in the field of remote sensing; however, the view of remote sensing images usually covers a large area, and the size of the vehicle is small and the background is complex. Therefore, compared with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem. In this paper, we propose a single-scale rapid convolutional neural network (SSRD-Net). In the proposed framework, we design a global relational (GR) block to enhance the fusion of local and global features; moreover, we adjust the image segmentation method to unify the vehicle size in the input image, thus simplifying the model structure and improving the detection speed. We further introduce an aerial remote sensing image dataset with rotating bounding boxes (RO-ARS), which has complex backgrounds such as snow, clouds, and fog scenes. We also design a data augmentation method to get more images with clouds and fog. Finally, we evaluate the performance of the proposed model on several datasets, and the experimental results show that the recall and precision are improved compared with existing methods.