NORMA eResearch @NCI Library

Smartphone‐based object recognition with embedded machine learning intelligence for unmanned aerial vehicles

Martinez-Alpiste, Ignacio, Casaseca-de-la-Higuera, Pablo, Alcaraz-Calero, Jose M., Grecos, Christos and Wang, Qi (2019) Smartphone‐based object recognition with embedded machine learning intelligence for unmanned aerial vehicles. Journal of Field Robotics. ISSN 1556-4967 (In Press)

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1002/rob.21921

Abstract

Existing artificial intelligence solutions typically operate in powerful platforms with high computational resources availability. However, a growing number of emerging use cases such as those based on unmanned aerial systems (UAS) require new solutions with embedded artificial intelligence on a highly mobile platform. This paper proposes an innovative UAS that explores machine learning (ML) capabilities in a smartphone‐based mobile platform for object detection and recognition applications. A new system framework tailored to this challenging use case is designed with a customized workflow specified. Furthermore, the design of the embedded ML leverages TensorFlow, a cutting‐edge open‐source ML framework. The prototype of the system integrates all the architectural components in a fully functional system, and it is suitable for real‐world operational environments such as seek and rescue use cases. Experimental results validate the design and prototyping of the system and demonstrate an overall improved performance compared with the state of the art in terms of a wide range of metrics.

Item Type: Article
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 12:03
Last Modified: 21 Nov 2019 12:03
URI: http://trap.ncirl.ie/id/eprint/4102

Actions (login required)

View Item View Item