Person Detection and Geolocation Estimation in UAV Aerial Images: An Experimental Approach

Sasa Sambolek, Marina Ivasic-Kos

2024

Abstract

The use of drones in SAR operations has become essential to assist in the search and rescue of a missing or injured person, as it reduces search time and costs, and increases the surveillance area and safety of the rescue team. Detecting people in aerial images is a demanding and tedious task for trained humans as well as for detection algorithms due to variations in pose, occlusion, scale, size, and location where a person may be in the image, as well as poor shooting conditions, poor visibility, blur due to movement and the like. In this paper, the YOLOv8 generic object detection model pre-trained on the COCO dataset is fine-tuned on the customized SARD dataset used to optimize the model for person detection on aerial images of mountainous landscapes, which are captured by drone. Different models of the YOLOv8 family algorithms fine-tuned on the SARD set were experimentally tested and it was shown that the YOLOv8x model achieves the highest mean average precision (mAP@0.5:0.95) of 63.8%, with an inference time of 4.6 ms which shows potential for real-time use in SARD operations. We have tested three geolocation algorithms in real conditions and proposed modification and recommendations for using in SAR missions for determining the geolocation of a person recorded by drone after automatic detection with the YOLOv8x model.

Paper Citation

in Harvard Style

Sambolek S. and Ivasic-Kos M. (2024). Person Detection and Geolocation Estimation in UAV Aerial Images: An Experimental Approach. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 785-792. DOI: 10.5220/0012411600003654

in Bibtex Style

@conference{icpram24,
author={Sasa Sambolek and Marina Ivasic-Kos},
title={Person Detection and Geolocation Estimation in UAV Aerial Images: An Experimental Approach},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={785-792},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012411600003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Person Detection and Geolocation Estimation in UAV Aerial Images: An Experimental Approach
SN - 978-989-758-684-2
AU - Sambolek S.
AU - Ivasic-Kos M.
PY - 2024
SP - 785
EP - 792
DO - 10.5220/0012411600003654
PB - SciTePress