|本期目录/Table of Contents|

[1]刘欣宜,张宝峰,符烨,等.基于深度学习的污染场地作业人员着装规范性检测[J].中国安全生产科学技术,2020,16(7):169-175.[doi:10.11731/j.issn.1673-193x.2020.07.027]
　LIU Xinyi,ZHANG Baofeng,FU Ye,et al.Detection on normalization of operating personnel dressing at contaminated sites based on deep learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2020,16(7):169-175.[doi:10.11731/j.issn.1673-193x.2020.07.027]
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基于深度学习的污染场地作业人员着装规范性检测
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
16
期数:
2020年7期
页码:
169-175
栏目:
职业安全卫生管理与技术
出版日期:
2020-07-30

文章信息/Info

Title:
Detection on normalization of operating personnel dressing at contaminated sites based on deep learning
文章编号:
1673-193X(2020)-07-0169-07
作者:
刘欣宜; 张宝峰; 符烨; 朱均超
(1.天津理工大学 计算机科学与工程学院，天津 300384；
2.天津理工大学 天津市复杂系统控制理论及应用重点实验室，天津 300384)
Author(s):
LIU Xinyi;  ZHANG Baofeng;  FU Ye;  ZHU Junchao
(1.School of Computer Science and Engineering,Tianjin University of Technology,Tianjin 300384,China;
2.Tianjin Key Laboratory for Control Theory & Applications in Complicated System,Tianjin University of Technology,Tianjin 300384,China)
关键词:
深度学习; 损失函数; 目标检测; 污染场地
Keywords:
deep learning;  loss function;  target detection;  contaminated sites
分类号:
X913;TP391.4;TP18
DOI:
10.11731/j.issn.1673-193x.2020.07.027
文献标志码:
A
摘要:
为解决污染场地修复作业中缺乏统一的人员安全保障管理措施以及人工监管困难的问题，结合HSE理念提出1种基于优化Faster R-CNN的作业人员着装规范性检测算法。该方法在回归损失函数中引入L2正则项，既保证模型的泛化能力，又提高深层网络模型收敛速度。基于自建着装规范数据集（Dress Code Dataset）进行实验，评价算法检测时间和mAP等指标。结果表明：所提出的着装规范性检测算法检测时间为44 ms，mAP为88.17%，解决了传统检测算法中实时性和准确率低的问题，且模型具有更好的泛化性和鲁棒性。
Abstract:
In order to solve the problem about the lacking of unified management measures for personnel safety guarantee and the difficulty of manual supervision in the remediation operation of contaminated sites,an algorithm for the detection on the normalization of operators dressing based on the optimized Faster R-CNN was put forward combining with the HSE concept.The L2 regularization term was introduced into the regression loss function,which not only guaranteed the generalization ability of the model,but also improved the convergence speed of the deep network model.The experiments were carried out on the self built Dress Code Dataset,and the detection time,mAP and other indexes of the algorithm were evaluated.The results showed that the detection time of the proposed algorithm for the detection on dressing normalization was 44 ms,and mAP was 88.17%,which solved the problem of low realtime and accuracy in the traditional detection algorithms,and the model has better generalization and robustness.

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备注/Memo

备注/Memo:
收稿日期： 2020-04-09
* 基金项目： 天津市互联网跨界融合创新科技重大专项项目（18ZXRHSF00240）；天津市科技计划项目(18YFCZZC00320)
作者简介： 刘欣宜，博士研究生，主要研究方向为深度学习、计算机视觉与图像处理。
通信作者： 朱均超，博士，副教授，主要研究方向为智能检测、机器视觉。
更新日期/Last Update: 2020-08-06