Benchmarking machine-learning-based object detection on a UAV and mobile platform

Ignacio Martinez-Alpiste, Pablo Casaseca-de-la-Higuera, Jose M. Alcaraz-Calero, Christos Grecos, Qi Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Citations (Scopus)
189 Downloads (Pure)


Object detection systems mounted on Unmanned Aerial Vehicles (UAVs) have gained momentum in recent years in light of the widespread use cases enabled by such systems in public safety and other areas. Machine learning has emerged as an enabler for improving the performance of object detection. However, there is little existing work that has studied the performance of the machine learning approach, which is computationally resource demanding, in a portable mobile platform for UAV based object detection in user mobility scenarios. This paper evaluates an integrated real-world testbed for this scenario, by employing commercial-off-the-shelf devices including a UAV system and a machine-learning-enabled mobile platform. It presents benchmarking results about the performance of popular machine learning and computer vision frameworks such as TensorFlow and OpenCV and the associated algorithms such as YOLO, embedded in a smartphone execution environment of limited resources. The results highlight opportunities and provide insights into technical gaps to be filled to realize real-time machine-learning-based object detection on a mobile platform with constrained resources.
Original languageEnglish
Title of host publication2019 IEEE Wireless Communications and Networking Conference (WCNC)
Number of pages6
ISBN (Electronic)9781538676462
ISBN (Print)9781538676479
Publication statusPublished - 30 Apr 2019

Publication series

NameIEEE Proceedings
ISSN (Print)1525-3511
ISSN (Electronic)1558-2612


  • Machine learning
  • Object detection
  • Image processing
  • Mobile platform
  • UAV


Dive into the research topics of 'Benchmarking machine-learning-based object detection on a UAV and mobile platform'. Together they form a unique fingerprint.

Cite this