【摘要】 为解决森林火灾检测模型部署难题，提出改进YOLOv8n的轻量化算法：采用GhostConv轻量化卷积替代部分传统卷积，引入C2f-Faster模块优化特征融合流程，通过整合多尺度特征从而提升复杂场景适应能力；结合C2f-Faster-EMA模块增强模型对微弱且模糊烟雾以及初期微小火焰目标的特征捕捉能力；利用Efficient Detect模块简化检测头结构。实验结果显示，优化后的模型参数量减少36.7%，GFLOPs降低了51.9%，mAP@0.5提升了1.4%，在轻量化基础上提升了检测精度。更多还原

【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.更多还原