loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Runqi Li ; Cen Chen and Mengyao Chen

Affiliation: Beijing University of Technology, China

Keyword(s): Deep learning, UAV, Regional classification, Image segmentation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: In order to improve the efficiency of urban traffic operation, this paper combines deep learning technology and UAV photography of drones. With the algorithm in this paper, we can classify traffic area and static area in high quality and speed. We make a test in Fengtai District of Beijing to conduct regional traffic identification research on the traffic content of the area. The primary identification area includes the vehicle travel area and coordinates the pedestrian area in a coordinated manner. The main research result of this algorithm is to propose a key frame extraction scheme for UAV image and then combine it with the application of Mask R-CNN in high-altitude image to identify the ground area. The experimental results are similar to the same algorithm (refer to FCNs for this article). Comparative benchmarks) have obvious advantages of high speed and high accuracy, which are of great help to traffic safety and urban planning.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.145.59.187

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Li, R.; Chen, C. and Chen, M. (2019). Research on UAV Image Classification Based on Deep Learning. In Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - CTISC; ISBN 978-989-758-357-5, SciTePress, pages 148-152. DOI: 10.5220/0008098701480152

@conference{ctisc19,
author={Runqi Li. and Cen Chen. and Mengyao Chen.},
title={Research on UAV Image Classification Based on Deep Learning},
booktitle={Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - CTISC},
year={2019},
pages={148-152},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008098701480152},
isbn={978-989-758-357-5},
}

TY - CONF

JO - Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - CTISC
TI - Research on UAV Image Classification Based on Deep Learning
SN - 978-989-758-357-5
AU - Li, R.
AU - Chen, C.
AU - Chen, M.
PY - 2019
SP - 148
EP - 152
DO - 10.5220/0008098701480152
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic