Real-time Vehicle Detection from UAV Imagery

Fast and accurate vehicle detection in unmanned aerial vehicle (UAV) imagery is a meaningful but challenging task, playing an important role in a wide range of applications. Due to its tiny size, few features, variable scales and imbalance vehicle sample problems in UAV imagery, current deep learning methods used in this task cannot achieve a satisfactory performance both in accuracy and speed, which is obvious a classical trade-off problem. In this paper, we propose a single-shot vehicle detector, which focuses on accurate and real-time vehicle detection in UAV imagery. We make contributions in the following two aspects: 1) presenting a multi-scale feature fusion module to combine the high resolution but semantically weak features with the low resolution but semantically strong features, aiming to introduce context information to enhance the feature representation of the small vehicles; 2) proposing a dynamic training strategy (DTS) which constructs the network to learn more discriminative features of hard examples, via using cross entropy and focal loss function alternately. Experimental results show that our method can achieve 90.8% accuracy in UAV images and can run at 59 FPS on a single NVIDIA 1080Ti GPU for the small vehicle detection in UAV images.