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Fire and smoke detection is a critical component of intelligent surveillance and disaster early warning systems, with wide applications in forest fire prevention, industrial safety and other fields. However, existing algorithms often suffer from low detection precision, slow speed, and large model size under natural environments. To address these issues, this paper proposes a fire and smoke detection method based on the lightweight YOLOv8n. The proposed model replaces the original backbone with PP-LCNet to reduce model size, introduces the CARAFE upsampling operator to enhance feature reconstruction, and integrates the EMA attention mechanism to improve target perception capability. Experimental results show that, compared with the original YOLOv8n, the improved model reduces parameters by 1.01 M and computational cost by 2.2 G, while achieving a detection precision of 94.8% and an mAP50 of 93.6%. It outperforms other mainstream lightweight detection models, achieving an excellent balance between precision and real-time performance, and demonstrates strong practical value.

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火焰与烟雾检测作为智能监控与灾害预警的重要环节，广泛应用于森林防火、工业安全等领域。然而，现有算法在自然环境下往往面临检测精确率低、速度慢、模型过大等问题。为此，本文提出一种基于轻量化YOLOv8n的火焰烟雾检测方法。该方法采用PP-LCNet替换原有主干网络以减小模型规模，引入CARAFE上采样算子提升特征重建能力，并融合EMA注意力机制以增强目标感知能力。实验结果显示，该改进模型相比YOLOv8n参数量减少1.01 M，计算量降低2.2 G，同时在检测精度和mAP50分别达到94.8%和93.6%，在多种主流轻量化检测模型中表现最佳，兼具精确性与实时性，具备较高应用价值。

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^4．黑龙江省网络化与智能控制重点实验室 大庆 163318, bio={"content":"

路敬祎，教授，博士，博士生导师，主要研究方向为油气管网检测、信号处理及人工智能等。E-mail: ljywdm@126.com

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路敬祎，教授，博士，博士生导师，主要研究方向为油气管网检测、信号处理及人工智能等。E-mail: ljywdm@126.com

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