| 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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| 引用本文： | 韩豫,张泾杰,孙昊,姚佳玥,尤少迪.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. |
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| 作者姓名： | 韩豫 张泾杰 孙昊 姚佳玥 尤少迪 |
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| 作者单位： | （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） |
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| 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。
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| 关 键 词： | 施工安全 图像识别 建筑工人 安全检查 深度图像 |
Design and implementation of intelligent safety inspection
system for construction workers based on image recognition |
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| HAN Yu,ZHANG Jingjie,SUN Hao,YAO Jiayue,YOU Shaodi.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. |
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| Authors: | HAN Yu ZHANG Jingjie SUN Hao YAO Jiayue YOU Shaodi |
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| Affiliation: | 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 |
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| 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. |
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| Keywords: | construction safety image recognition construction worker safety inspection depth image |
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