LUFFD-YOLO: A Lightweight Model for UAV Remote Sensing Forest Fire Detection Based on Attention Mechanism and Multi-Level Feature Fusion
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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-and-play 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.
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YOLO has also been applied to monitor environmental hazards, such as forest fires.
“YOLO has also been applied to monitor environmental hazards, such as forest fires [109,110].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [109]
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Shi, B., Zhu, Y., Li, J., Wu, X. F., Wang, J., Li, Z., & Liu, H. (2026) · Remote Sensing · doi:10.3390/rs18183066
Wang, D., Yang, K., Fang, J., Li, Y., Ai, D., Yan, J., & Tang, J. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3737693
YOLOv11-Fire: an intelligent fire detection model based on visible and infrared fusion images
Wu, Y., Shi, B., Yang, C., Zhou, X., Jiang, T., Chen, Y., & Zheng, L. (2026) · Measurement · doi:10.1016/j.measurement.2026.123594
Zhu, F., Im, J., Yang, G., Zhao, Y., Zhao, F., & Zhen, Z. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3742890
Self-citationLiu, J., Ren, R., Wang, S., Feng, J., He, E., Xue, W., Li, X., & Hu, J. (2026) · Frontiers in Forests and Global Change · doi:10.3389/ffgc.2026.1906504
- Authors
- Han, Y., Duan, B., Guan, R., Yang, G., & Zhen, Z.
- Affiliations
- Northeast Forestry University (CN)
- National University of Defense Technology (CN)
- Corresponding author
- Zhen, Zhen
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2024-06-15
- Volume
- 16
- Issue
- 12
- Pages
- 2177
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W4399749893
- Cited by
- 46
- References
- 56
- Citations by year
- 2026: 23 · 2025: 17 · 2024: 6
- FWCI
- 11.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key R&D Program of China (2023YFC3006900, DC-2024471)
- National College Students Innovation and Entrepreneurship Training Program (2023YFC3006900, DC-2024471)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Fire Detection and Safety Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Keywords
- forest fire detection, UAV imagery, lightweight object detection, YOLOv8, attention mechanism, multi-level feature fusion, GhostNetV2, small object detection, hierarchical feature fusion, wildfire monitoring
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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