A Machine Learning Based Method for Object Detection and Localization Using a Monocular RGB Camera Equipped Drone

Abdulrahman Javaid; Miswar Akhtar Syed; Uthman Baroudi

doi:10.1109/IWCMC58020.2023.10182369

A Machine Learning Based Method for Object Detection and Localization Using a Monocular RGB Camera Equipped Drone

Abdulrahman Javaid^*, Miswar Akhtar Syed, Uthman Baroudi

^*Corresponding author for this work

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

8 Scopus citations

Abstract

Growing Unmanned Aerial Vehicle (UAV) market trends and interest in potential uses such as monitoring, visual inspection, object detection, and path planning have shown promising results using machine learning techniques. However, UAV adoption faces several challenges in real-life scenarios as lowaccuracy sensors are involved in the identification, tracking, and localization of UAVs. In order to overcome the aforementioned challenges, this paper proposes an intelligent machine learningbased system coupled with computer vision (CV) to detect objects and localize UAVs equipped with just a monocular camera. The experimental results using the Telo DJI drone demonstrate that the proposed methodology can detect, track objects, and localize the drone with high accuracy. The system's ability for automated monitoring in real environments can lend its uses for urban traffic, logistics, and security applications.

Original language	English
Title of host publication	2023 International Wireless Communications and Mobile Computing, IWCMC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	144-149
Number of pages	6
ISBN (Electronic)	9798350333398
DOIs	https://doi.org/10.1109/IWCMC58020.2023.10182369
State	Published - 2023
Event	19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023 - Hybrid, Marrakesh, Morocco Duration: 19 Jun 2023 → 23 Jun 2023

Publication series

Name	2023 International Wireless Communications and Mobile Computing, IWCMC 2023

Conference

Conference	19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023
Country/Territory	Morocco
City	Hybrid, Marrakesh
Period	19/06/23 → 23/06/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Autonomous navigation system
DJI Telo drone
deep learning
depth map
localization
monocular camera
object detection

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Safety, Risk, Reliability and Quality

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IWCMC58020.2023.10182369

Cite this

Javaid, A., Syed, M. A., & Baroudi, U. (2023). A Machine Learning Based Method for Object Detection and Localization Using a Monocular RGB Camera Equipped Drone. In 2023 International Wireless Communications and Mobile Computing, IWCMC 2023 (pp. 144-149). (2023 International Wireless Communications and Mobile Computing, IWCMC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWCMC58020.2023.10182369

Javaid, Abdulrahman ; Syed, Miswar Akhtar ; Baroudi, Uthman. / A Machine Learning Based Method for Object Detection and Localization Using a Monocular RGB Camera Equipped Drone. 2023 International Wireless Communications and Mobile Computing, IWCMC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 144-149 (2023 International Wireless Communications and Mobile Computing, IWCMC 2023).

@inproceedings{2dd4bb1844044aac907a7dde8cad7bcb,

title = "A Machine Learning Based Method for Object Detection and Localization Using a Monocular RGB Camera Equipped Drone",

abstract = "Growing Unmanned Aerial Vehicle (UAV) market trends and interest in potential uses such as monitoring, visual inspection, object detection, and path planning have shown promising results using machine learning techniques. However, UAV adoption faces several challenges in real-life scenarios as lowaccuracy sensors are involved in the identification, tracking, and localization of UAVs. In order to overcome the aforementioned challenges, this paper proposes an intelligent machine learningbased system coupled with computer vision (CV) to detect objects and localize UAVs equipped with just a monocular camera. The experimental results using the Telo DJI drone demonstrate that the proposed methodology can detect, track objects, and localize the drone with high accuracy. The system's ability for automated monitoring in real environments can lend its uses for urban traffic, logistics, and security applications.",

keywords = "Autonomous navigation system, DJI Telo drone, deep learning, depth map, localization, monocular camera, object detection",

author = "Abdulrahman Javaid and Syed, {Miswar Akhtar} and Uthman Baroudi",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023 ; Conference date: 19-06-2023 Through 23-06-2023",

year = "2023",

doi = "10.1109/IWCMC58020.2023.10182369",

language = "English",

series = "2023 International Wireless Communications and Mobile Computing, IWCMC 2023",

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

pages = "144--149",

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address = "United States",

}

Javaid, A, Syed, MA & Baroudi, U 2023, A Machine Learning Based Method for Object Detection and Localization Using a Monocular RGB Camera Equipped Drone. in 2023 International Wireless Communications and Mobile Computing, IWCMC 2023. 2023 International Wireless Communications and Mobile Computing, IWCMC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 144-149, 19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023, Hybrid, Marrakesh, Morocco, 19/06/23. https://doi.org/10.1109/IWCMC58020.2023.10182369

A Machine Learning Based Method for Object Detection and Localization Using a Monocular RGB Camera Equipped Drone. / Javaid, Abdulrahman; Syed, Miswar Akhtar; Baroudi, Uthman.
2023 International Wireless Communications and Mobile Computing, IWCMC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 144-149 (2023 International Wireless Communications and Mobile Computing, IWCMC 2023).

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

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AU - Syed, Miswar Akhtar

AU - Baroudi, Uthman

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N2 - Growing Unmanned Aerial Vehicle (UAV) market trends and interest in potential uses such as monitoring, visual inspection, object detection, and path planning have shown promising results using machine learning techniques. However, UAV adoption faces several challenges in real-life scenarios as lowaccuracy sensors are involved in the identification, tracking, and localization of UAVs. In order to overcome the aforementioned challenges, this paper proposes an intelligent machine learningbased system coupled with computer vision (CV) to detect objects and localize UAVs equipped with just a monocular camera. The experimental results using the Telo DJI drone demonstrate that the proposed methodology can detect, track objects, and localize the drone with high accuracy. The system's ability for automated monitoring in real environments can lend its uses for urban traffic, logistics, and security applications.

AB - Growing Unmanned Aerial Vehicle (UAV) market trends and interest in potential uses such as monitoring, visual inspection, object detection, and path planning have shown promising results using machine learning techniques. However, UAV adoption faces several challenges in real-life scenarios as lowaccuracy sensors are involved in the identification, tracking, and localization of UAVs. In order to overcome the aforementioned challenges, this paper proposes an intelligent machine learningbased system coupled with computer vision (CV) to detect objects and localize UAVs equipped with just a monocular camera. The experimental results using the Telo DJI drone demonstrate that the proposed methodology can detect, track objects, and localize the drone with high accuracy. The system's ability for automated monitoring in real environments can lend its uses for urban traffic, logistics, and security applications.

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KW - depth map

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Javaid A, Syed MA, Baroudi U. A Machine Learning Based Method for Object Detection and Localization Using a Monocular RGB Camera Equipped Drone. In 2023 International Wireless Communications and Mobile Computing, IWCMC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 144-149. (2023 International Wireless Communications and Mobile Computing, IWCMC 2023). doi: 10.1109/IWCMC58020.2023.10182369

A Machine Learning Based Method for Object Detection and Localization Using a Monocular RGB Camera Equipped Drone

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this