Drone Detection and Identification System using Artificial Intelligence

Dongkyu Rroyr Lee; Woong Gyu La; Hwangnam Kim

doi:10.1109/ICTC.2018.8539442

Drone Detection and Identification System using Artificial Intelligence

Dongkyu Rroyr Lee, Woong Gyu La, Hwangnam Kim

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

As drone attracts much interest, the drone industry has opened their market to ordinary people, making drones to be used in daily lives. However, as it got easier for drone to be used by more people, safety and security issues have raised as accidents are much more likely to happen: colliding into people by losing control or invading secured properties. For safety purposes, it is essential for observers and drone to be aware of an approaching drone. In this paper, we introduce a comprehensive drone detection system based on machine learning. This system is designed to be operable on drones with camera. Based on the camera images, the system deduces location on image and vendor model of drone based on machine classification. The system is actually built with OpenCV library. We collected drone imagery and information for learning process. The system's output shows about 89 percent accuracy.

Original language	English
Title of host publication	9th International Conference on Information and Communication Technology Convergence
Subtitle of host publication	ICT Convergence Powered by Smart Intelligence, ICTC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1131-1133
Number of pages	3
ISBN (Electronic)	9781538650400
DOIs	https://doi.org/10.1109/ICTC.2018.8539442
Publication status	Published - 2018 Nov 16
Event	9th International Conference on Information and Communication Technology Convergence, ICTC 2018 - Jeju Island, Korea, Republic of Duration: 2018 Oct 17 → 2018 Oct 19

Other

Other	9th International Conference on Information and Communication Technology Convergence, ICTC 2018
Country	Korea, Republic of
City	Jeju Island
Period	18/10/17 → 18/10/19

Keywords

artificial intelligence
classifier
deep learning
detection
image processing

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Information Systems
Information Systems and Management
Artificial Intelligence

Access to Document

10.1109/ICTC.2018.8539442

Fingerprint Dive into the research topics of 'Drone Detection and Identification System using Artificial Intelligence'. Together they form a unique fingerprint.

Cite this

Lee, D. R., Gyu La, W., & Kim, H. (2018). Drone Detection and Identification System using Artificial Intelligence. In 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018 (pp. 1131-1133). [8539442] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTC.2018.8539442

Drone Detection and Identification System using Artificial Intelligence. / Lee, Dongkyu Rroyr; Gyu La, Woong; Kim, Hwangnam.

9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1131-1133 8539442.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, DR, Gyu La, W & Kim, H 2018, Drone Detection and Identification System using Artificial Intelligence. in 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018., 8539442, Institute of Electrical and Electronics Engineers Inc., pp. 1131-1133, 9th International Conference on Information and Communication Technology Convergence, ICTC 2018, Jeju Island, Korea, Republic of, 18/10/17. https://doi.org/10.1109/ICTC.2018.8539442

@inproceedings{93228ba3de9a46178a5158ec8fe829c8,

title = "Drone Detection and Identification System using Artificial Intelligence",

abstract = "As drone attracts much interest, the drone industry has opened their market to ordinary people, making drones to be used in daily lives. However, as it got easier for drone to be used by more people, safety and security issues have raised as accidents are much more likely to happen: colliding into people by losing control or invading secured properties. For safety purposes, it is essential for observers and drone to be aware of an approaching drone. In this paper, we introduce a comprehensive drone detection system based on machine learning. This system is designed to be operable on drones with camera. Based on the camera images, the system deduces location on image and vendor model of drone based on machine classification. The system is actually built with OpenCV library. We collected drone imagery and information for learning process. The system's output shows about 89 percent accuracy.",

keywords = "artificial intelligence, classifier, deep learning, detection, image processing",

author = "Lee, {Dongkyu Rroyr} and {Gyu La}, Woong and Hwangnam Kim",

year = "2018",

month = nov,

day = "16",

doi = "10.1109/ICTC.2018.8539442",

language = "English",

pages = "1131--1133",

booktitle = "9th International Conference on Information and Communication Technology Convergence",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "9th International Conference on Information and Communication Technology Convergence, ICTC 2018 ; Conference date: 17-10-2018 Through 19-10-2018",

}

TY - GEN

T1 - Drone Detection and Identification System using Artificial Intelligence

AU - Lee, Dongkyu Rroyr

AU - Gyu La, Woong

AU - Kim, Hwangnam

PY - 2018/11/16

Y1 - 2018/11/16

N2 - As drone attracts much interest, the drone industry has opened their market to ordinary people, making drones to be used in daily lives. However, as it got easier for drone to be used by more people, safety and security issues have raised as accidents are much more likely to happen: colliding into people by losing control or invading secured properties. For safety purposes, it is essential for observers and drone to be aware of an approaching drone. In this paper, we introduce a comprehensive drone detection system based on machine learning. This system is designed to be operable on drones with camera. Based on the camera images, the system deduces location on image and vendor model of drone based on machine classification. The system is actually built with OpenCV library. We collected drone imagery and information for learning process. The system's output shows about 89 percent accuracy.

AB - As drone attracts much interest, the drone industry has opened their market to ordinary people, making drones to be used in daily lives. However, as it got easier for drone to be used by more people, safety and security issues have raised as accidents are much more likely to happen: colliding into people by losing control or invading secured properties. For safety purposes, it is essential for observers and drone to be aware of an approaching drone. In this paper, we introduce a comprehensive drone detection system based on machine learning. This system is designed to be operable on drones with camera. Based on the camera images, the system deduces location on image and vendor model of drone based on machine classification. The system is actually built with OpenCV library. We collected drone imagery and information for learning process. The system's output shows about 89 percent accuracy.

KW - artificial intelligence

KW - classifier

KW - deep learning

KW - detection

KW - image processing

UR - http://www.scopus.com/inward/record.url?scp=85059458820&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85059458820&partnerID=8YFLogxK

U2 - 10.1109/ICTC.2018.8539442

DO - 10.1109/ICTC.2018.8539442

M3 - Conference contribution

AN - SCOPUS:85059458820

SP - 1131

EP - 1133

BT - 9th International Conference on Information and Communication Technology Convergence

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th International Conference on Information and Communication Technology Convergence, ICTC 2018

Y2 - 17 October 2018 through 19 October 2018

ER -

Drone Detection and Identification System using Artificial Intelligence

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Cite this