现代信息科技2026，Vol.10Issue(4)：55-59,5.DOI:10.19850/j.cnki.2096-4706.2026.04.010
基于YOLOv8n的轻量化森林火灾烟雾检测算法研究
Research on a Lightweight Forest Fire Smoke Detection Algorithm Based on YOLOv8n
摘要
Abstract
To solve the deployment challenge of the forest fire detection model,a lightweight algorithm for enhancing YOLOv8n is put forward:employing GhostConv lightweight convolution to substitute some traditional convolutions;introducing the C2f-Faster module to optimize the feature fusion process,thereby enhancing adaptability to complex scenarios by integrating multi-scale features;combining the C2f-Faster-EMA module to strengthen the model's ability to capture features of faint and blurred smoke and early tiny flames;utilizing the Efficient Detect module to simplify the structure of the detection head.Experimental outcomes demonstrate that the optimized model reduces the parameter quantity by 36.7%,lowers GFLOPs by 51.9%,and raises the mean average precision by 1.4 percentage points,enhancing the detection accuracy on the basis of being lightweight.关键词
森林火灾烟雾检测/YOLOv8n/轻量化/注意力机制Key words
fire smoke detection/YOLOv8n/lightweighting/Attention Mechanism分类
信息技术与安全科学引用本文复制引用
王晓靖,吴俊杰,郭瑞程,席晨然..基于YOLOv8n的轻量化森林火灾烟雾检测算法研究[J].现代信息科技,2026,10(4):55-59,5.基金项目
山西省科技战略研究专项重点项目(202304031401011) （202304031401011）
