Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)

<Previous Article In Volume
Next Article In Volume>

Detecting Forest Fires by UAVs Based on Improved YOLOv7

Authors
Yicheng Jin1, *
1School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
*Corresponding author. Email: 22013348@mail.ecust.edu.cn
Corresponding Author
Yicheng Jin
Available Online 31 August 2025.
DOI
10.2991/978-94-6463-821-9_65How to use a DOI?
Keywords
YOLOv7; MobileNetv3; forest fire
Abstract

The purpose of this study is to construct an improved forest fire detection model to improve the accuracy and real-time performance of UAV fire detection in forest environment. By introducing MobileNetv3 into YOLOv7 as the backbone network, its lightweight and efficient feature extraction capabilities can be used to optimize the model to lower its computational overhead and improve the detection speed, so as to ensure that the UAV can quickly respond to fire signals with limited hardware resources. The innovation of this research lies in breaking through the limitations of the traditional YOLOv7 backbone network architecture, using MobileNetv3 to realize the lightweight of the model, optimizing the feature fusion and connection mode, strengthening the model’s learning capability for forest fire features, reducing the computational complexity by 61% and improving the average accuracy and speed, more accurately identifying fire signs in complex forest environments, and improving the overall efficiency of the UAV fire detection system.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
Series
Advances in Engineering Research
Publication Date
31 August 2025
ISBN
978-94-6463-821-9
ISSN
2352-5401
DOI
10.2991/978-94-6463-821-9_65How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yicheng Jin
PY  - 2025
DA  - 2025/08/31
TI  - Detecting Forest Fires by UAVs Based on Improved YOLOv7
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 665
EP  - 674
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_65
DO  - 10.2991/978-94-6463-821-9_65
ID  - Jin2025
ER  -