YOLOv7E: An Attention-Based Improved YOLOv7 for the Detection of Unmanned Aerial Vehicles

Dapinder Kaur, Dapinder Kaur, Neeraj Battish, Arnav Bhavsar, Shashi Poddar, Shashi Poddar

2024

Abstract

The detection of Unmanned Aerial Vehicles (UAVs) is a special case for object detection, specifically in the case of air-to-air scenarios with complex backgrounds. The proliferated use of UAVs in commercial, noncommercial, and defense applications has raised concerns regarding their unauthorized usage and mishandling in certain instances. Deep learning-based architectures developed recently to deal with this challenge could detect UAVs very efficiently in different backgrounds. However, the problem of detecting UAVs in complex background environments need further improvement and has been catered here by incorporating an attention mechanism in the YOLOv7 architecture, which considers channel and spatial attention. The proposed model is trained with the DeTFly dataset, and its performance has been evaluated in terms of detection rate, precision, and mean average precision values. The experimental results present the effectiveness of the proposed YOLOv7E architecture for detecting UAVs in aerial scenarios.

Paper Citation

in Harvard Style

Kaur D., Battish N., Bhavsar A. and Poddar S. (2024). YOLOv7E: An Attention-Based Improved YOLOv7 for the Detection of Unmanned Aerial Vehicles. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 344-351. DOI: 10.5220/0012391500003654

in Bibtex Style

@conference{icpram24,
author={Dapinder Kaur and Neeraj Battish and Arnav Bhavsar and Shashi Poddar},
title={YOLOv7E: An Attention-Based Improved YOLOv7 for the Detection of Unmanned Aerial Vehicles},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={344-351},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012391500003654},
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 - YOLOv7E: An Attention-Based Improved YOLOv7 for the Detection of Unmanned Aerial Vehicles
SN - 978-989-758-684-2
AU - Kaur D.
AU - Battish N.
AU - Bhavsar A.
AU - Poddar S.
PY - 2024
SP - 344
EP - 351
DO - 10.5220/0012391500003654
PB - SciTePress