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Benchmarking Machine-Learning-Based Object Detection on a UAV and Mobile Platform

Martinez-Alpiste, Ignacio, Casaseca-de-la-Higuera, Pablo, Alcaraz-Calero, Jose, Grecos, Christos and Wang, Qi (2019) Benchmarking Machine-Learning-Based Object Detection on a UAV and Mobile Platform. In: 2019 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, p. 8885504. ISBN 9781538676462

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/WCNC.2019.8885504

Abstract

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.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

Q Science > QA Mathematics > Computer software > Mobile Phone Applications
T Technology > T Technology (General) > Information Technology > Computer software > Mobile Phone Applications
Divisions: School of Computing > Staff Research and Publications
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 21 Nov 2019 11:41
Last Modified: 21 Nov 2019 11:49
URI: http://trap.ncirl.ie/id/eprint/4101

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