双模型算法耦合识别井下人员不安全行为的方法

doi:10.16265/j.cnki.issn1003-3033.2026.02.1372

中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (2): 18-26.doi: 10.16265/j.cnki.issn1003-3033.2026.02.1372

双模型算法耦合识别井下人员不安全行为的方法

谭波¹(), 隋龙琨¹^,^**(), 柯巍², 刘衍², 朱权洁³, 何宁³

¹ 中国矿业大学(北京) 应急管理与安全工程学院,北京 100083
² 湖北省烟草公司十堰市公司,湖北十堰 442000
³ 华北科技学院应急技术与管理学院,河北三河 065201

收稿日期:2025-09-10 修回日期:2025-11-18 出版日期:2026-02-28
通信作者:
^** 隋龙琨(1997—),男,山东济宁人,博士研究生,主要研究方向为煤矿安全风险识别。E-mail: suilk1997@163.com。
作者简介:
谭波 (1981—),男,湖南株洲人,博士,教授,主要从事煤矿火灾防治等方面的研究。E-mail:tanbo709@126.com。
朱权洁, 教授;
何宁, 副教授
基金资助:
国家自然科学基金资助(51774291); 国家自然科学基金资助(51864045); 国家重点研发计划(2023YFC3009101); 河北省自然科学基金资助(E2022508046)

Method of coupling identifying unsafe behaviors of underground personnel based on dual-model algorithm

TAN Bo¹(), SUI Longkun¹^,^**(), KE Wei², LIU Yan², ZHU Quanjie³, HE Ning³

¹ School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
² Shiyan Company of Hubei Tobacco Company, Shiyan Hubei 442000, China
³ School of Emergency Technology and Management, North China Institute of Science and Technology, Sanhe Hebei 065201, China

Received:2025-09-10 Revised:2025-11-18 Published:2026-02-28

摘要/Abstract

摘要：

为了防止由于井下人员的不安全行为造成的安全事故,并保障井下人员安全,利用先进的机器视觉和计算机技术,改进传统的YOLOv5s算法和OpenPose算法目标检测模型,提出一种双模型耦合识别井下人员不安全行为的算法;通过统计分析当前煤矿井下较为常见的不安全行为,将矿工不安全行为分为物品、动作、区域等3类;根据矿工的不安全行为特点,采用改进YOLOv5s算法和OpenPose算法耦合识别的方法,在公共数据集和自建数据集上进行训练和验证。结果表明:相比于当前主流的方法,双模型耦合识别方法在自建数据集和公共数据集的基础上识别准确率有较大提升(5%~10%),可对井下人员不安全行为进行快速、有效的识别。

关键词: 井下人员, 不安全行为, 双模型算法, 耦合识别, 目标检测

Abstract:

In order to prevent safety accidents caused by unsafe behaviors of underground personnel and to ensure their safety, by utilizing advanced machine vision and computer technologies, the traditional YOLOv5s algorithm and OpenPose algorithm target detection models were improved, and a dual-model coupled algorithm for identifying unsafe behaviors of underground personnel was proposed. Through statistical analysis of the most common unsafe behaviors in current underground coal mines, the unsafe behaviors of miners were classified, including item-related, action-related, and area-related unsafe behaviors. According to the characteristics of miners' unsafe behaviors, the improved YOLOv5s algorithm and the OpenPose algorithm were coupled for recognition, and training and verification were conducted on public datasets and self-built datasets. The results show that compared with the current mainstream methods, the dual-model coupled recognition method has a significant improvement in recognition accuracy on self-built datasets and public datasets, with an increase of 5% to 10%, and can quickly and effectively identify unsafe behaviors of underground personnel.

Key words: underground personnel, unsafe behaviors, dual-model algorithm, coupling identifying, object detection

中图分类号:

X936

谭波, 隋龙琨, 柯巍, 刘衍, 朱权洁, 何宁. 双模型算法耦合识别井下人员不安全行为的方法[J]. 中国安全科学学报, 2026, 36(2): 18-26.

TAN Bo, SUI Longkun, KE Wei, LIU Yan, ZHU Quanjie, HE Ning. Method of coupling identifying unsafe behaviors of underground personnel based on dual-model algorithm[J]. China Safety Science Journal, 2026, 36(2): 18-26.

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不安全行为分类	矿井场景样本数目	非矿井场景样本数目
物品	4 300	4 900
动作	7 200	6 200
区域	3 000	5 400
总计	14 500	16 500

模型	数据集	mAP@0.5/%	mAP@0.5: 0.95/%	参数量/ 10⁷个	时间/ ms	FPS/ (帧·s^-1)
SSD	COCO数据集	63.5	41.3	4.79	52	50
SSD	自制数据集	74.5	44.8	3.96	49	53
YOLOv5s	COCO数据集	74.7	43.5	4.43	47	54
YOLOv5s	自制数据集	82.8	46.9	3.84	41	59
YOLOv7	COCO数据集	79.6	45.7	3.78	44	56
YOLOv7	自制数据集	86.4	48.5	3.54	39	57
YOLOv7-tiny	COCO数据集	85.2	45.3	3.88	44	59
YOLOv7-tiny	自制数据集	89.4	49.8	3.62	40	63
OpenPose-MobileNetV1	COCO数据集	90.2	51.3	3.47	42	64
OpenPose-MobileNetV1	自制数据集	93.4	54.4	2.85	41	68
OpenPose-LSTM	COCO数据集	91.2	52.7	3.13	39	60
OpenPose-LSTM	自制数据集	92.6	53.6	3.02	35	66
文中方法	COCO数据集	93.2	55.3	2.58	35	65
文中方法	自制数据集	96.3	58.1	2.76	33	67

双模型算法耦合识别井下人员不安全行为的方法

Method of coupling identifying unsafe behaviors of underground personnel based on dual-model algorithm

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