Published January 7, 2020 | Version v1
Conference paper Open

Informed Region Selection for Efficient UAV-based Object Detectors: Altitude-aware Vehicle Detection with CyCAR Dataset

  • 1. Imperial College,Dept. of Electrical and Electronic Engineering
  • 2. KIOS Center of Excellence, University of Cyprus


Deep Learning-based 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 low-latency constraints during inference, in order to make mission-critical decisions in real-time. In this paper, we address the challenge of efficient deployment of region-based object detectors in aerial imagery, by introducing an informed methodology for extracting candidate detection regions (proposals). Our approach considers information from the UAV on-board sensors, such as flying altitude and light-weight computer vision filters, along with prior domain knowledge to intelligently decrease the number of region proposals by eliminating false-positives 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 state-of-the-art detection models can achieve up to 2.6x faster inference by employing our altitude-aware data-driven methodology. Alongside, we introduce and provide to the community a novel vehicle-annotated and altitude-stamped dataset of real UAV imagery, captured at numerous flying heights under a wide span of traffic scenarios.


* This dataset excludes proprietary images. 


© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, in-cluding reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to serv-ers or lists, or reuse of any copyrighted component of this work in other works. A. Kouris, C. Kyrkou and C. Bouganis, "Informed Region Selection for Efficient UAV-based Object Detectors: Altitude-aware Vehicle Detection with CyCAR Dataset," 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019, pp. 51-58. doi: 10.1109/IROS40897.2019.8967722



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KIOS CoE – KIOS Research and Innovation Centre of Excellence 739551
European Commission