Counting Vehicles with Deep Learning in Onboard UAV Imagery The integration of mobile and ubiquitous computing with deep learning methods is a promising emerging trend that aims at moving the processing task closer to the data source rather than bringing the data to a central node. The advantages of this approach range from bandwidth reduction, high scalability, to high reliability, just to name a few. In this paper, we propose a realtime deep learning approach to automatically detect and count vehicles in videos taken from a UAV (Unmanned Aerial Vehicle). Our solution relies on a convolutional neural networkbased model finetuned to the specific domain of applications that is able to precisely localize instances of the vehicles using a regression approach, straight from image pixels to bounding box coordinates, reasoning globally about the image when making predictions and implicitly encoding contextual information. A comprehensive experimental evaluation on realworld datasets shows that our approach results in stateoftheart performances. Furthermore, our solution achieves realtime performances by running at a speed of 4 Frames Per Second on an NVIDIA Jetson TX2 board, showing the potentiality of this approach for realtime processing in UAVs.

Learningbased Object Detection in High Resolution UAV Images: An Empirical Study Deep learningbased methods are continuously boosting the performance of detecting objects in natural images. On the contrary, detecting objects in Unmanned Aerial Vehicle (UAV) images remains to be a difficult task in the field of computer vision, due to the challenge of training a wellperformed detection model working on UAV images which usually contain instances with varied orientations, scales, and contours, etc. Furthermore, only a few researchers have focused on this field, probably because of difficulties in UAV data acquisition and labelling. Inspired by this, we collected a largescale dataset with multiscale and highresolution UAV images, named MOHR, which contains 10,631 images captured by a UAV affixed with three kinds of cameras. Since these images were captured in a suburban environment, we manually annotated five classes of objects, including car, truck, building, collapse and flood damage. An empirical study was then conducted by adopting six advanced object detection methods all of which are based on deep learning technologies. The results indicate the great potential of these evaluated object detection models, but also reveal that the research on such a challenging UAV dataset using current deep learning techniques is far reaching.