2020
DOI: 10.3390/s20164420
|View full text |Cite
|
Sign up to set email alerts
|

A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs

Abstract: The use of UAVs for remote sensing is increasing. In this paper, we demonstrate a method for evaluating and selecting suitable hardware to be used for deployment of algorithms for UAV-based remote sensing under considerations of Size, Weight, Power, and Computational constraints. These constraints hinder the deployment of rapidly evolving computer vision and robotics algorithms on UAVs, because they require intricate knowledge about the system and architecture to allow for effective implementation. We propose … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
4
2

Relationship

1
5

Authors

Journals

citations
Cited by 6 publications
(1 citation statement)
references
References 38 publications
0
1
0
Order By: Relevance
“…However, low average precision values for the best performing models (i.e., 17.41% on the VisDrone2019 dataset compared to 40.6% on the MS-COCO dataset) implies that further efforts in CNN architectures for object detection from airborne UAV data are required. Future implementations of CNN models discussed above could require a separate study of any model performance impacts caused by the streaming and processing of high-resolution frames onboard sub-2 kg UAVs, and onboard inference of CNN models in embedded computing systems, such as the UP 2 , Jetson Nano, and Edge TPU [45,46].…”
Section: Discussionmentioning
confidence: 99%
“…However, low average precision values for the best performing models (i.e., 17.41% on the VisDrone2019 dataset compared to 40.6% on the MS-COCO dataset) implies that further efforts in CNN architectures for object detection from airborne UAV data are required. Future implementations of CNN models discussed above could require a separate study of any model performance impacts caused by the streaming and processing of high-resolution frames onboard sub-2 kg UAVs, and onboard inference of CNN models in embedded computing systems, such as the UP 2 , Jetson Nano, and Edge TPU [45,46].…”
Section: Discussionmentioning
confidence: 99%