An Improved YOLOv5 for Real-time Mini-UAV Detection in No Fly Zones

Tijeni Delleji, Tijeni Delleji, Zied Chtourou

2022

Abstract

In the past few years, the manufacturing technology of mini-UAVs has undergone major developments. Therefore, the early warning optical drone detection, as an important part of intelligent surveillance, is becoming a global research hotspot. In this article, the authors provide a prospective study to prevent any potential hazards that mini-UAVs may cause, especially those that can carry payloads. Subsequently, we regarded the problem of detecting and locating mini-UAVs in different environments as the problem of detecting tiny and very small objects from an air image. However, the accuracy and speed of existing detection algorithms do not meet the requirements of real-time detection. For solving this problem, we developed a mini-UAV detection model based on YOLOv5. The main contributions of this research are as follows: (1) a mini-UAV dataset of air pictures was prepared using Dahua multi-sensor camera; (2) a tiny and very small object detection layers are added to improve the model’s ability to detect mini-UAVs. The experimental results show that the overall performance of the improved YOLOv5 is better than the original. Therefore, the proposed mini-UAV detection technology can be deployed in monitor center in order to protect a No Fly Zone or a restricted area.

Paper Citation

in Harvard Style

Delleji T. and Chtourou Z. (2022). An Improved YOLOv5 for Real-time Mini-UAV Detection in No Fly Zones. In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-563-0, pages 174-181. DOI: 10.5220/0011065400003209

in Bibtex Style

@conference{improve22,
author={Tijeni Delleji and Zied Chtourou},
title={An Improved YOLOv5 for Real-time Mini-UAV Detection in No Fly Zones},
booktitle={Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2022},
pages={174-181},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011065400003209},
isbn={978-989-758-563-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - An Improved YOLOv5 for Real-time Mini-UAV Detection in No Fly Zones
SN - 978-989-758-563-0
AU - Delleji T.
AU - Chtourou Z.
PY - 2022
SP - 174
EP - 181
DO - 10.5220/0011065400003209