西北农林科技大学学报（自然科学版）2026，Vol.54Issue(9)：49-58,10.DOI:10.13207/j.jnwafu.2026.09.005
基于改进YOLOv8n的小目标林火监测模型
Improved YOLOv8n-based model for small-target forest fire monitoring
摘要
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 loca-tions.[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 de-crease 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 ex-periments 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 de-tection performance and lower deployment cost.Furthermore,CAM visualization and quantitative analysis re-vealed 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.关键词
森林火灾/火灾监测/YOLOv8/小目标林火Key words
forest fire/fire monitoring/YOLOv8/small-target forest fire分类
农业科技引用本文复制引用
薛丰昌,佟雅慧,陈笑娟,秦晓波,杨猛..基于改进YOLOv8n的小目标林火监测模型[J].西北农林科技大学学报（自然科学版）,2026,54(9):49-58,10.基金项目
河北省重点研发计划项目(22375421D) （22375421D）
