基于改进SBR-YOLOv11n的受限空间火灾探测模型

doi:10.16265/j.cnki.issn1003-3033.2026.09.1007

中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (9): 263-270.doi: 10.16265/j.cnki.issn1003-3033.2026.09.1007

• 智能安全技术 • 上一篇

基于改进SBR-YOLOv11n的受限空间火灾探测模型

李聪¹^,²(), 许文博¹^,², 滕文飞¹^,², 汪陈徽³, 吴建松²

¹ 煤炭精细勘探与智能开发全国重点实验室, 北京 100083
² 中国矿业大学(北京) 应急管理与安全工程学院, 北京 100083
³ 中国国家铁路集团有限公司铁路安全研究中心, 北京 100081

收稿日期:2026-04-26 修回日期:2026-07-20 出版日期:2026-10-20
作者简介:
李聪 (1991—),男,安徽淮南人,博士,副教授,主要从事受限空间火灾监测、低温边界油池燃烧等方面的研究。E-mail:18600156862@163.com。
吴建松教授
基金资助:
煤炭精细勘探与智能开发全国重点实验室2023年开放基金资助(SKLCRSM23KFA02); 贵州省科技支撑计划项目(黔科合支撑[2026]一般345); 国家自然科学基金资助(52304274); 中央高校基本科研业务费专项资金资助(2026ZKPYAQ01)

Fire detection model in confined space based on improved SBR-YOLOv11n

Li Cong¹^,²(), Xu Wenbo¹^,², Teng Wenfei¹^,², Wang Chenhui³, Wu Jiansong²

¹ SKL for Fine Exploration and Intelligent Development of Coal Resources, Beijing 100083, China
² School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
³ Railway Safety Research Center, China State Railway Group Co., Ltd., Beijing 100081, China

Received:2026-04-26 Revised:2026-07-20 Published:2026-10-20

摘要/Abstract

摘要：

针对受限空间火灾中火焰亮度低、目标小、易受干扰等问题,在YOLOv11n模型基础上,提出一种改进的受限空间火灾探测模型SBR-YOLOv11n。在骨干网络部分,将部分标准卷积层替换为空间深度转换卷积(SPDConv)层,解决模型在低亮度下火焰特征容易丢失的问题;在颈部网络部分,引入双向特征金字塔网络(BiFPN)模块,通过双向连接与可学习权重机制提升小目标火焰特征的融合效果;此外,采用倒置残差块注意力机制(iRMB),对动态火焰特征进行自适应加权,提高模型探测动态火焰的鲁棒性。结果表明:SBR-YOLOv11n模型各项评价指标达到最优,与YOLOv11n模型相比,精确率(P)、召回率(R)、平均精度均值(mAP@0.5和mAP@0.5:0.95)分别提升5.27%、3.81%、4.17%和3.03%,达到84.94%、83.83%、84.63%和62.97%,检测帧数(FPS)为75 帧/s。SBR-YOLOv11n模型满足受限空间火灾快速与精准探测需求,适用于受限空间火灾探测。

关键词: 受限空间, 火灾探测, SBR-YOLOv11n, 特征提取, 注意力机制

Abstract:

Aiming at the problems of low flame brightness, small target and easy interference in confined space fires, an improved confined space fire detection model SBR-YOLOv11n was proposed based on YOLOv11n model. In the backbone network part, some standard convolution layers were replaced by Space to Depth Convolution (SPDConv) layers to solve the problem that the flame features were easy to lose under low brightness. In the neck network part, the Bidirectional Feature Pyramid Network (BiFPN) module was introduced to improve the fusion effect of small target flame features through bidirectional connections and learnable weight mechanisms. In addition, the Inverted Residual Mobile Block (iRMB) was used to adaptively weight the dynamic flame features to improve the robustness of the model to dynamic flame detection. Experimental results show that the SBR-YOLOv11n model has the best evaluation indexes. Compared with the YOLOv11n model, Precision (P), Recall (R), mean Average Precision (mAP@0.5) and mAP@0.5:0.95 are increase by 5.27 %, 3.81 %, 4.17 % and 3.03 % respectively, reaching 84.94 %, 83.83 %, 84.63 % and 62.97 %, with a detection speed of 75 Frames Per Second (FPS). The SBR-YOLOv11n model meets the needs of rapid and accurate fire detection in confined space and is suitable for fire detection in confined spaces.

Key words: confined space, fire detection, SPDConv-BiFPN-iRMB-YOLOv11n (SBR-YOLOv11n), feature extraction, attention mechanism

中图分类号:

X932爆炸安全与防火、防爆

李聪, 许文博, 滕文飞, 汪陈徽, 吴建松. 基于改进SBR-YOLOv11n的受限空间火灾探测模型[J]. 中国安全科学学报, 2026, 36(9): 263-270.

Li Cong, Xu Wenbo, Teng Wenfei, Wang Chenhui, Wu Jiansong. Fire detection model in confined space based on improved SBR-YOLOv11n[J]. China Safety Science Journal, 2026, 36(9): 263-270.

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序号	YOLOv11n	SPDConv	BiFPN	iRMB	P/%	R/%	mAP@0.5/%	mAP@0.5: 0.95/%	FPS/(帧/s)
1	√	×	×	×	79.67	80.02	80.46	59.94	113
2	√	√	×	×	80.88	79.56	80.91	61.83	107
3	√	×	√	×	81.30	80.62	81.06	60.30	89
4	√	×	×	√	79.87	80.81	80.62	61.27	90
5	√	√	√	×	82.93	81.99	82.30	62.98	85
6	√	√	×	√	83.41	79.93	81.19	61.80	96
7	√	×	√	√	81.51	82.35	82.06	62.57	83
8	√	√	√	√	84.94	83.83	84.63	62.97	75

模型	P/%	R/%	mAP@ 0.5/%	mAP@0.5: 0.95/%	FPS/ (帧/s)
YOLOv5n	74.49	77.22	77.52	56.76	138
YOLOv8n	77.46	79.52	78.99	58.50	116
YOLOv10n	76.91	78.47	79.16	57.24	121
YOLOv11n	79.67	80.02	80.46	59.94	113
SBR- YOLOv11n	84.94	83.83	84.63	62.97	75

基于改进SBR-YOLOv11n的受限空间火灾探测模型

Fire detection model in confined space based on improved SBR-YOLOv11n

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