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Fire and smoke detection method based on lightweight YOLOv8n
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Jingyi Lu1, 2, 3, 4, Bo Chen1, 3, Yang Wu1, 3, Qihao Liang1, 3, Peng Wang1, 2, 3, 4
Electronic Measurement Technology | 2026, 49(6) : 211 - 219
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Electronic Measurement Technology | 2026, 49(6): 211-219
• Information Technology and Image Processing •
Fire and smoke detection method based on lightweight YOLOv8n
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Jingyi Lu1, 2, 3, 4, Bo Chen1, 3, Yang Wu1, 3, Qihao Liang1, 3, Peng Wang1, 2, 3, 4
Affiliations
  • 1.Sanya offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya 572024, China
  • 2.Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China
  • 3.School of Electrical Information Engineering, Northeast Petroleum University, Daqing 163318, China
  • 4.Key Laboratory of Networking and Intellectual Control System in Heilongjiang Province, Daqing 163318, China
doi: 10.19651/j.cnki.emt.2519437
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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.

flame smoke  /  deep learning  /  object detection  /  YOLOv8n  /  lightweight
路敬祎, 陈波, 吴阳, 梁棋皓, 王鹏. 基于轻量化YOLOv8n的火焰烟雾检测方法. 电子测量技术, 2026 , 49 (6) : 211 -219 . DOI: 10.19651/j.cnki.emt.2519437
Jingyi Lu, Bo Chen, Yang Wu, Qihao Liang, Peng Wang. Fire and smoke detection method based on lightweight YOLOv8n[J]. Electronic Measurement Technology, 2026 , 49 (6) : 211 -219 . DOI: 10.19651/j.cnki.emt.2519437
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Year 2026 volume 49 Issue 6
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Article Info
doi: 10.19651/j.cnki.emt.2519437
  • Receive Date：2025-07-24
  • Online Date：2026-05-15
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  • Received：2025-07-24
Funding
Affiliations
    1.Sanya offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya 572024, China
    2.Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China
    3.School of Electrical Information Engineering, Northeast Petroleum University, Daqing 163318, China
    4.Key Laboratory of Networking and Intellectual Control System in Heilongjiang Province, Daqing 163318, China
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