Real-Time Object Detection Based on Unmanned Aerial Vehicle

Abstract

In this paper, a real-time image detection system for unmanned aerial vehicle (UAV) is presented. First, the You Only Look Once (YOLO) detector is retrained to detect and recognize objects in UAV images fast and accurately. The retrained YOLO detector possesses a tradeoff between quickly objects identification and precisely objects localization, accounting for 4 general moving objects (i.e., car, bus, truck and pedestrian) in UAV images. Second, extra 4,000 UAV images shot by the embedded UAV camera are fed into the YOLO which is used to predict bounding-box with label probabilities from a full image. The YOLO is compared with the Faster Region Convolutional Neural Network, as well as other deep-learning frameworks for object detection. Field experiments on UAV videos shot in various backgrounds are performed to testify that the proposed system can detect objects in UAV images effectively and robustly, achieving a real-time detection speed at 15 frames per second and a satisfying accuracy on the wild test set of 400 UAV images with a Graphic Processing Unit (GPU) acceleration.