RLFNet: A Real-Time Lightweight Network for Forest Fire Detection on Edge Devices.
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| Title: | RLFNet: A Real-Time Lightweight Network for Forest Fire Detection on Edge Devices. |
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| Authors: | Huang, Zhengshen1 (AUTHOR), Kou, Weili1,2 (AUTHOR) kwl@swfu.edu.cn, Zheng, Chen2,3 (AUTHOR), Di, Guangzhi1 (AUTHOR), Zhang, Qixing2,3 (AUTHOR), Ma, Chenhao1,3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1543. 23p. |
| Subjects: | Edge computing, Deep learning, Optimization algorithms, Artificial neural networks, Real-time computing, Forest fire management, Feature extraction |
| Abstract: | Highlights: What are the main findings? RLFNet, a real-time lightweight network, is well suited for edge device deployment for real-time forest fire detection. On our self-constructed dataset, it achieves mAP50 = 76.5% and 224.8 FPS using only 1.9 million parameters and 5.0 GFLOPs, realizing an optimal balance between accuracy and efficiency. The model improves detection accuracy and inference speed while reducing parameter count and computational complexity by integrating three self-designed modules: a Parallel Multi-Scale Extraction Block (PMEB), a Bidirectional Cross Fusion Module (BCFM), and a Faster Inference Detection Head (FIDH). In addition, a pruning strategy is applied to further optimize the model. What are the implications of the main findings? The proposed model provides a practical, deployable solution for real-time forest fire detection on resource-constrained edge devices (e.g., UAVs, robots, and cameras), ensuring high detection accuracy and robust performance in complex forest environments. The results demonstrate that the collaborative lightweight design of the backbone, neck, and head networks, combined with adaptive pruning, effectively resolves the trade-off between high accuracy and computational efficiency seen in existing models. Forest fires cause severe ecological and economic losses, so timely and accurate detection becomes crucial for effective prevention and control. Edge devices with intelligent algorithms can detect forest fires in real time. Current deep learning algorithms can achieve high accuracy, but they are not suitable for edge devices because they require substantial computing resources. To address this issue, this study proposes a real-time lightweight forest fire detection network (RLFNet) improved from YOLOv11n, with three key enhancements to the backbone, neck, and head. (1) A Parallel Multi-Scale Extraction Block (PMEB) improves C3k2 with a dual-branch parallel strategy to enhance multi-scale feature extraction efficiency; (2) a Bidirectional Cross Fusion Module (BCFM) replaces simple Concat with a context-aware cross-gating mechanism to suppress background noise and reduce false alarms; and (3) a Faster Inference Detection Head (FIDH) leverages structural re-parameterization and group normalization to boost inference efficiency while reducing parameters. In addition, a Layer-Adaptive Magnitude-based Pruning (LAMP) strategy is applied to further improve model's computational efficiency. Experimental results on the self-constructed Diverse Fire Scenario (DFS) dataset demonstrate that RLFNet reduces parameters and GFLOPs by 25.2% and 20.6%, boosts mAP50 by 5.3%, and achieves an inference speed of 225 FPS, attaining the best accuracy and speed among the compared models. Validation on a public remote sensing dataset further confirms its strong generalization. These results indicate that RLFNet provides a high efficiency and lightweight solution for edge devices to real-time detect forest fires. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? RLFNet, a real-time lightweight network, is well suited for edge device deployment for real-time forest fire detection. On our self-constructed dataset, it achieves mAP50 = 76.5% and 224.8 FPS using only 1.9 million parameters and 5.0 GFLOPs, realizing an optimal balance between accuracy and efficiency. The model improves detection accuracy and inference speed while reducing parameter count and computational complexity by integrating three self-designed modules: a Parallel Multi-Scale Extraction Block (PMEB), a Bidirectional Cross Fusion Module (BCFM), and a Faster Inference Detection Head (FIDH). In addition, a pruning strategy is applied to further optimize the model. What are the implications of the main findings? The proposed model provides a practical, deployable solution for real-time forest fire detection on resource-constrained edge devices (e.g., UAVs, robots, and cameras), ensuring high detection accuracy and robust performance in complex forest environments. The results demonstrate that the collaborative lightweight design of the backbone, neck, and head networks, combined with adaptive pruning, effectively resolves the trade-off between high accuracy and computational efficiency seen in existing models. Forest fires cause severe ecological and economic losses, so timely and accurate detection becomes crucial for effective prevention and control. Edge devices with intelligent algorithms can detect forest fires in real time. Current deep learning algorithms can achieve high accuracy, but they are not suitable for edge devices because they require substantial computing resources. To address this issue, this study proposes a real-time lightweight forest fire detection network (RLFNet) improved from YOLOv11n, with three key enhancements to the backbone, neck, and head. (1) A Parallel Multi-Scale Extraction Block (PMEB) improves C3k2 with a dual-branch parallel strategy to enhance multi-scale feature extraction efficiency; (2) a Bidirectional Cross Fusion Module (BCFM) replaces simple Concat with a context-aware cross-gating mechanism to suppress background noise and reduce false alarms; and (3) a Faster Inference Detection Head (FIDH) leverages structural re-parameterization and group normalization to boost inference efficiency while reducing parameters. In addition, a Layer-Adaptive Magnitude-based Pruning (LAMP) strategy is applied to further improve model's computational efficiency. Experimental results on the self-constructed Diverse Fire Scenario (DFS) dataset demonstrate that RLFNet reduces parameters and GFLOPs by 25.2% and 20.6%, boosts mAP50 by 5.3%, and achieves an inference speed of 225 FPS, attaining the best accuracy and speed among the compared models. Validation on a public remote sensing dataset further confirms its strong generalization. These results indicate that RLFNet provides a high efficiency and lightweight solution for edge devices to real-time detect forest fires. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101543 |
