|本期目录/Table of Contents|

[1]蔡晨晖,梁晓刚,师剑雄,等.矿山视频大数据智能分析与安全生产监控平台研究*[J].中国安全生产科学技术,2024,20(1):65-70.[doi:10.11731/j.issn.1673-193x.2024.01.010]
　CAI Chenhui,LIANG Xiaogang,SHI Jianxiong,et al.Research on mine video big data intelligent analysis and work safety monitoring platform[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(1):65-70.[doi:10.11731/j.issn.1673-193x.2024.01.010]
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矿山视频大数据智能分析与安全生产监控平台研究*
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
20
期数:
2024年1期
页码:
65-70
栏目:
职业安全卫生管理与技术
出版日期:
2024-01-31

文章信息/Info

Title:
Research on mine video big data intelligent analysis and work safety monitoring platform
文章编号:
1673-193X(2024)-01-0065-06
作者:
蔡晨晖; 梁晓刚; 师剑雄; 白艳
(1.甘肃建投绿色建材产业发展集团有限公司，甘肃 兰州 730000；
2.中国科学院地理科学与资源研究所，北京 100101)
Author(s):
CAI Chenhui;  LIANG Xiaogang;  SHI Jianxiong;  BAI Yan
(1.Gansu Jiantou Green Building Materials Industry Development Group,Lanzhou Gansu 730000,China;
2.Institute of Geographic Sciences and Natural Resources Research,CAS,Beijing 100101,China)
关键词:
目标检测; 矿山安全监管; YOLOv5; 三维可视化; 监控系统
Keywords:
object detection;  mining safety supervision;  YOLOv5;  3D visualization;  monitoring system
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2024.01.010
文献标志码:
A
摘要:
为了实现对现代化大型矿山生产的全时段、全过程、全域覆盖的安全监测与监管，提出集成地理信息技术、5G技术、三维矿区及装备模型、遥感与视频监测、人工智能等多项信息技术的整体技术方案，并协同设计智慧矿山安全生产监控系统平台。研究结果表明：基于视频大数据智能分析技术，遴选视频目标自动检测算法，集成对YOLOv5算法进行Mosaic-9数据增强、K-means聚类先验锚框与损失函数优化等改进技术，可实现现实场景中安全帽佩戴、反光衣穿戴以及烟雾火灾等视频自动化识别监测功能与虚拟三维模型的可视化融合，可为实现矿山安全全天候智能化、可视化监管提供分析平台。
Abstract:
In order to realize the safety monitoring and supervision of modern large-scale mining production with whole time,whole process,and whole area coverage,an overall technical scheme integrating the geographic information technology,5G technology,3D mining area and equipment model,remote sensing and video monitoring,artificial intelligence and other information technologies was proposed,and the intelligent mine work safety monitoring system platform was designed collaboratively.The results show that based on the video big data intelligent analysis technology,the automatic detection algorithm of video target is selected,and the Mosaic-9 data augmentation,K-means clustering prior anchor box and loss function optimization of YOLOv5 algorithm are integrated,so as to realize the visual integration of video automatic recognition and monitoring functions such as helmet wearing,reflective clothing wearing and smoke fire in real scenes with virtual three-dimensional models,which can provide an analysis platform for realizing the all-weather intelligent visual supervision of mine safety.

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备注/Memo

备注/Memo:
收稿日期： 2023-07-12
* 基金项目： 兰州市科技计划项目(2021-1-20);甘肃省科技计划项目(22CX8JA144)
作者简介： 蔡晨晖，本科，工程师，主要研究方向为绿色智慧矿山建设及矿山安全管理。
通信作者： 白艳，硕士研究生，工程师，主要研究方向为绿色智慧矿山建设及矿山安全管理。
更新日期/Last Update: 2024-02-19