Informed Region Selection for Efficient UAVbased Object Detectors: Altitudeaware Vehicle Detection with CyCAR Dataset Deep Learningbased object detectors enhance the capabilities of remote sensing platforms, such as Unmanned Aerial Vehicles (UAVs), in a wide spectrum of machine vision applications. However, the integration of deep learning introduces heavy computational requirements, preventing the deployment of such algorithms in scenarios that impose lowlatency constraints during inference, in order to make missioncritical decisions in realtime. In this paper, we address the challenge of efficient deployment of regionbased object detectors in aerial imagery, by introducing an informed methodology for extracting candidate detection regions (proposals). Our approach considers information from the UAV onboard sensors, such as flying altitude and lightweight computer vision filters, along with prior domain knowledge to intelligently decrease the number of region proposals by eliminating falsepositives at an early stage of the computation, reducing significantly the computational workload while sustaining the detection accuracy. We apply and evaluate the proposed approach on the task of vehicle detection. Our experiments demonstrate that stateoftheart detection models can achieve up to 2.6x faster inference by employing our altitudeaware datadriven methodology. Alongside, we introduce and provide to the community a novel vehicleannotated and altitudestamped dataset of real UAV imagery, captured at numerous flying heights under a wide span of traffic scenarios.

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.