Human Detection in Disaster Scenarios for Enhanced Emergency Response Using YOLO11

Md Sadiq Z Pattankudi, Samarth Uppin, Abdul Rafay Attar, Kunal Bhoomaraddi, Rohan Kolhar, Sneha Varur

2025

Abstract

In disaster scenarios, the ability to rapidly and accurately detect key elements such as rescuers, victims, vehicles, and dangerous objects is crucial for effective and timely rescue operations. This research proposes applying You Only Look Once(YOLO11), a real time object detection model to detect aerial images captured by drone. To train the model, a custom dataset was created, containing four classes—rescuer, victim, vehicles, and dangerous objects—representing critical components in disaster environments. This dataset provided a comprehensive and controlled environment to evaluate the system’s performance. The results demonstrated that the proposed system significantly enhances decision making processes, particularly in locating and rescuing human survivors during emergency situations. The model achieved an overall precision of 82.4%, recall of 30.5%, a mean Average Precision at IoU 50 (mAP50) of 36.1%, and a mean Average Precision (mAP) at IoU 50-95 (mAP50- 95) of 16.4%. These performance metrics highlight the reliability of the model in identifying critical objects in real time, with opportunities for further refinement to improve recall and precision balance, making it a valuable tool for disaster response teams.

Paper Citation

in Harvard Style

Pattankudi M., Uppin S., Attar A., Bhoomaraddi K., Kolhar R. and Varur S. (2025). Human Detection in Disaster Scenarios for Enhanced Emergency Response Using YOLO11. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 739-746. DOI: 10.5220/0013601000004664

in Bibtex Style

@conference{incoft25,
author={Md Sadiq Z Pattankudi and Samarth Uppin and Abdul Rafay Attar and Kunal Bhoomaraddi and Rohan Kolhar and Sneha Varur},
title={Human Detection in Disaster Scenarios for Enhanced Emergency Response Using YOLO11},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={739-746},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013601000004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Human Detection in Disaster Scenarios for Enhanced Emergency Response Using YOLO11
SN - 978-989-758-763-4
AU - Pattankudi M.
AU - Uppin S.
AU - Attar A.
AU - Bhoomaraddi K.
AU - Kolhar R.
AU - Varur S.
PY - 2025
SP - 739
EP - 746
DO - 10.5220/0013601000004664
PB - SciTePress