| 摘要: |
| 【 目的】基于传统森林火灾深度学习监测模型对小型火源实时精确监测的局限性，提出一种改进YO? LOv8n模型的森林火灾监测模型YOLOv8n_MB。【 方 法】在YOLOv8n的基础上，引入混合局部通道注意力（MLCA）模块，增强模型对小火源关键特征的敏感度；通过加权双向特征金字塔（BiFPN）实现跨尺度高效特征融合， 以提升小目标监测能力，最终建立森林火灾监测模型YOLOv8n_MB。设计消融试验验证各改进模块的有效性，并结 合CAM可视化技术及热力图重合度（intersection over Union of CAM，IoU_CAM）、显著性能量比（energy ratio，ER）、 最大响应点命中率（pointing-game，PG）等量化指标分析模型，验证“模型关注区域与真实火点”的一致性。【 结果】与YOLOv8n相比，YOLOv8n_MB模型的识别精确率（Precision）及平均精确度均值 mA@0.5和mA@0.5：0.95分别提高2 8，3.7和2.1个百分点，召回率（Recall）下降了0.2个百分点，火点识别能力优于YOLOv8其他系列模型。改进后的YOLOv8n_MB模型在保持计算效率的同时显著提升了监测精度，能够快速、准确监测地面火源。消融试验表明， 与原始YOLOv8n模型相比，YOLOv8n_MB模型的参数量减少0.19 MB，平均精确度（average precision，AP）提升2.4个百分点，具备更优的森林火灾监测性能和较低部署成本。CAM可视化与量化分析显示，YOLOv8n_MB在大范围、 小目标及复杂场景下的林火识别能力均优于其他模型。【 结论】YOLOv8n_MB模型实现了精度与轻量化的平衡，具 备良好的小目标森林火灾监测能力。 |
| 关键词: 森林火灾 火灾监测 YOLOv8 小目标林火 |
| DOI：10. 13207/j. jnwafu. 2026. 09. 005 |
| 分类号: |
| 基金项目:河北省重点研发计划项目（22375421D） |
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| Improved YOLOv8n⁃based model for small⁃target forest fire monitoring |
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XUE Fengchang1, TONG Yahui1, CHEN Xiaojuan2, QIN Xiaobo3, YANG Meng1
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1.School of Remote Sensing and Geomatics Engineering，Nanjing University of Information Science and Technology，Nanjing， Jiangsu 210044，China;2.Meteorological Disaster Prevention and Environmental Meteorology Center of Hebei Province， Shijiazhuang，Hebei 050021，China;3.Shijiazhuang Meteorological Bureau of Hebei Province，Shijiazhuang，Hebei 050081，China
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| Abstract: |
| 【 Objective】To address the limitations of traditional deep learning models in the real-time and accurate monitoring of small forest fire sources，this study proposes an improved YOLOv8n-based forest fire de? tection model，named YOLOv8n_MB.【Method】Based on the YOLOv8n architecture，a Mixed Local Channel Attention（MLCA） module was introduced to enhance the model’s sensitivity to key features of small fire sources. In addition，a Weighted Bidirectional Feature Pyramid Network（BiFPN） was employed to achieve effi? cient cross-scale feature fusion，thereby improving the small-target detection capability. The resulting forest fire monitoring model，named YOLOv8n_MB，was further evaluated through ablation experiments to verify the effectiveness of each improved module. Moreover，Class Activation Map（CAM） visualization，together with quantitative metrics such as the intersection over union of CAM（IoU_CAM），Energy Ratio（ER），and Pointing Game（PG），was used to analyze the consistency between the model’s attention regions and actual fire locations.【Result】Compared with YOLOv8，the proposed YOLOv8n_MB model achieved improvements of 2.8，3.7，and 2.1 percentage points in detection precision，mAP@0.5， and mAP@0. 5：0.95，respectively，with a decrease of 0.2 percentage points in Recall. This demonstrated its superior accuracy in fire spot detection over other YOLOv8 variants. The improved YOLOv8n_MB model significantly enhanced detection precision while maintaining computational efficiency，enabling fast and accurate monitoring of ground fire sources. Ablation experiments showed that，compared with the original YOLOv8n，YOLOv8n_MB reduced the parameters by 0.19 MB and increased the Average Precision（AP） by 2.4 percentage points，demonstrating superior forest fire detection performance and lower deployment cost. Furthermore，CAM visualization and quantitative analysis revealed that YOLOv8n_MB exhibited stronger forest fire recognition capability across large-scale，small-target，and complex scenarios than other models.【Conclusion】The YOLOv8n_MB model achieves a balance between detection accuracy and lightweight design，demonstrating strong capability on small-target forest fire monitoring. |
| Key words: forest fire fire monitoring YOLOv8 small-target forest fire |
