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.