LUFFD-YOLO: A Lightweight Model for UAV Remote Sensing Forest Fire Detection Based on Attention Mechanism and Multi-Level Feature Fusion
Date:2025-06-02 clicks:
Impact Factor:5.0
DOI number:10.3390/rs16122177
Journal:remote sensing
Key Words:attention mechanism; feature fusion; forest fire; lightweight network; UAV remote sensing images
Abstract:The timely and precise detection of forest fires is critical for halting the spread of wildfires and minimizing ecological and economic damage. However, the large variation in target size and the complexity of the background in UAV remote sensing images increase the difficulty of real-time forest fire detection. To address this challenge, this study proposes a lightweight YOLO model for UAV remote sensing forest fire detection (LUFFD-YOLO) based on attention mechanism and multi-level feature fusion techniques: (1) GhostNetV2 was employed to enhance the conventional convolution in YOLOv8n for decreasing the number of parameters in the model; (2) a plug-andplay enhanced small-object forest fire detection C2f (ESDC2f) structure was proposed to enhance the detection capability for small forest fires; (3) an innovative hierarchical feature-integrated C2f (HFIC2f) structure was proposed to improve the model’s ability to extract information from complex backgrounds and the capability of feature fusion. The LUFFD-YOLO model surpasses the YOLOv8n, achieving a 5.1% enhancement in mAP and a 13% reduction in parameter count and obtaining desirable generalization on different datasets, indicating a good balance between high accuracy and model efficiency. This work would provide significant technical support for real-time forest fire detection using UAV remote-sensing images.
Co-author:Bingchen Duan,Renxiang Guan,Guang Yang
First Author:Q1, Yuhang Han
Indexed by:Journal paper
Correspondence Author:Zhen Zhen*
Volume:16
Issue:12
Page Number:2177
ISSN No.:2072-4292
Translation or Not:no
Date of Publication:2024-01-01
Included Journals:SCI