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403023

Enhancing Disaster Response Efforts with YOLOv8-based Human Detection in Mobile Robotics

Article

Last updated: 07 Jan 2025

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Tags

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Abstract

In the aftermath of natural disasters, the swift detection of individuals trapped beneath debris is crucial for successful rescue operations. This paper presents a Mobile Controlled Robot with advanced human detection capabilities designed to expedite search and rescue missions, emphasizing the importance of rapid response to save lives. Utilizing a YOLOv8 model with 90% accuracy, the robot analyzes real-time images captured by a webcam to detect human forms and movements, triggering a buzzer alert to notify rescue teams upon identifying potential victims.

The robot's remote operation via a mobile interface enhances flexibility and adaptability in complex terrains, allowing rescue personnel to control it from a safe distance. Equipped with all-terrain wheels, obstacle-avoidance sensors, and a thermal imaging camera, the robot can navigate through rubble and confined spaces, even in low visibility conditions. The mobile interface provides real-time video feed and sensor data to the rescue team, enabling quick, informed decision-making.

The robot's modular design allows for easy upgrades and maintenance, making it a cost-effective long-term solution. Rigorous testing has demonstrated the system's efficacy and reliability in accurately locating trapped individuals, offering a promising improvement in the efficiency and effectiveness of disaster response operations.

DOI

10.21608/iiis.2025.292458.1036

Keywords

Robotics, Yolo, Deep learning, Fire rescue

Authors

First Name

khaled

Last Name

Alnabulseih

MiddleName

-

Affiliation

Department of Artificial intelligence, Misr university for science and technology

Email

khaledalnabulseih@gmail.com

City

Giza

Orcid

-

First Name

Abd-EL-Rahman

Last Name

Abd-EL-Rehim

MiddleName

-

Affiliation

Department of Artificial intelligence, Misr university for science and technology

Email

94152@must.edu.eg

City

Giza

Orcid

-

First Name

Mohamed

Last Name

Badawi

MiddleName

Badawi

Affiliation

Department of Artificial Intelligence, College of Information Technology, Misr University for Science and Technology (MUST), 6th of October City 12566, Egypt

Email

mohammed.badawi@must.edu.eg

City

Cairo

Orcid

0000-0001-6218-160X

First Name

Rania

Last Name

Elgohary

MiddleName

-

Affiliation

Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

Email

rania.elgohary@cis.asu.edu.eg

City

-

Orcid

-

Volume

2

Article Issue

1

Related Issue

52805

Issue Date

2025-01-01

Receive Date

2024-05-25

Publish Date

2025-01-01

Online ISSN

2682-258X

Link

https://iiis.journals.ekb.eg/article_403023.html

Detail API

http://journals.ekb.eg?_action=service&article_code=403023

Order

5

Type

Original Article

Type Code

3,047

Publication Type

Journal

Publication Title

International Integrated Intelligent Systems

Publication Link

https://iiis.journals.ekb.eg/

MainTitle

Enhancing Disaster Response Efforts with YOLOv8-based Human Detection in Mobile Robotics

Details

Type

Article

Created At

07 Jan 2025