|本期目录/Table of Contents|

[1]韩豫,张泾杰,孙昊,等.基于图像识别的建筑工人智能安全检查系统设计与实现[J].中国安全生产科学技术,2016,12(10):142-148.[doi:10.11731/j.issn.1673-193x.2016.10.024]
 HAN Yu,ZHANG Jingjie,SUN Hao,et al.Design and implementation of intelligent safety inspection system for construction workers based on image recognition[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2016,12(10):142-148.[doi:10.11731/j.issn.1673-193x.2016.10.024]
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基于图像识别的建筑工人智能安全检查系统设计与实现
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
12
期数:
2016年10期
页码:
142-148
栏目:
现代职业安全卫生管理与技术
出版日期:
2016-10-30

文章信息/Info

Title:
Design and implementation of intelligent safety inspection system for construction workers based on image recognition
作者:
韩豫1 张泾杰1 孙昊1 姚佳玥1 尤少迪2
(1. 江苏大学 土木工程与力学学院,江苏镇江212013; 2.澳洲国立大学 国家信息通信技术中心,澳大利亚澳洲首都领地2601)
Author(s):
HAN Yu1 ZHANG Jingjie1 SUN Hao1 YAO Jiayue1 YOU Shaodi2
1. Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang Jiangsu 212013, China; 2. National ICT Australia and Australian National University, Australian Capital Territory, 2601, Australia
关键词:
施工安全图像识别建筑工人安全检查深度图像
Keywords:
construction safety image recognition construction worker safety inspection depth image
分类号:
X924.2
DOI:
10.11731/j.issn.1673-193x.2016.10.024
文献标志码:
A
摘要:
为提高建筑工人作业前安全检查的效率和效果,减少事故发生。以图像识别技术为核心支撑,提出了建筑工人智能安全检查系统的结构、功能及运行流程,并对系统运行效果进行了测试。研究和测试表明:该系统具备身份识别、安全装备检查、作业行为能力检查功能,能实现建筑工人作业前的自动、智能安全检查。该系统的身份识别正确率为83.75%、安全帽识别正确率为96.25%、安全带识别正确率为63.75%。该系统具有硬件投入低、检测速度快、准确性高、应用场景广泛的特点。
Abstract:
In order to improve the efficiency and effectiveness of safety inspection for construction workers before operation and reduce accidents, the framework, functions and operational process of the intelligent safety inspection system based on image recognition for construction workers were promoted, and the operation effect of the system was tested. The results showed that the system has the functions of identity recognition, safety equipment inspection and operation behavior capacity checking, and it can realize the automatic and intelligent safety inspection on construction workers before operation. The identity recognition accuracy of the system is 83.75%, the accuracy of safety helmet identification is 96.25%, and the accuracy of safety belt identification is 63.75%. The system has the features of low hardware investment, fast inspection speed, high accuracy and wide application scenarios.

参考文献/References:

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备注/Memo

备注/Memo:
国家自然科学基金项目(51408266,51308113);教育部人文社会科学研究青年基金项目(14YJCZH047);江苏大学基金项目(14JDG012)
更新日期/Last Update: 2016-11-30