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Identification Research on the Miner’s Safety Helmet Wear Based on Convolutional Neural Network

BI Lin 1,2,XIE Wei 1,2,CUI Jun 1,2   

  1. 1.School of Resources and Safety Engineering,Central South University,Changsha    410083,Hunan,China;
    2.Center of Digital Mine Research,Central South University,Changsha    410083,Hunan,China
  • Received:2016-06-27 Revised:2017-02-15 Online:2017-08-30 Published:2017-10-30

Abstract:

In order to solve human factors restrictions of mine safety monitoring,which relied on manual monitoring of video data to identify risk factors,a method of construction of deep convolution network to identity whether miners wear helmets without adding any auxiliary device.Images was extracted from video data,which was divided into three categories:background,miners wearing helmets and miners without helmets,through rotated offset and sheared for image.The experimental companison is made by constructing three different levels of convolutional neural networks.Experiment shows that deep convolution network which was developed by“4 convolution layers+3 pooling layers+3 fully connected layers”has a highest recognition rate,reached 91.2%.Convolution neural network can achieve intelligent identification of miners dress safety.Research show that intelligent recognition of mine has an important reference for safety monitoring,safe behavior and security status .

Key words: mine safety, convolution neural network, intelligent identification, helmet, safety in production, Caffe deep learning framework

CLC Number: 

  • X936

[1] Ji Xuewen.Study on the intelligent construction of new mines[J].China Mine Engineering,2015,44(2):60-64.[吉学文.新矿山智能化建设问题探讨[J].中国矿山工程,2015,44(2):60-64.]
[2] Hu Tian,Wang Xingang.Analysis and design of safety helmet recognition system based on wavelet transform and neural network[J].Software Guide,2006(23):37-39.[胡恬,王新刚.基于小波变换和神经网络的安全帽识别系统分析与设计[J].软件导刊,2006(23):37-39.]
[3] Liu Yunbo,Huang Hua.Research on monitoring of workers’helmet wearing at the construction site[J].Electronic Science and Techndogy,2015,28(4):69-72.[刘云波,黄华.施工现场安全帽佩戴情况监控技术研究[J].电子科技,2015,28(4):69-72.]
[4]  Yu Kai,Jia Lei,Chen Yuqiang,et al.Deep learning:Yester- day,today and tomorrow[J].Journal of Computer Research and Development,2013,50(9):1799-1804.余凯,贾磊,陈雨强,等.深度学习的昨天、今天和明天[J].计算机研究与发展,2013,50(9):1799-1804.
[5] Hinton G E,Osindero S,Teh Y W.A fast learning algo- rithm for deep belief nets[J].Neural Computer,2006,18(7): 1527-1554.
[6] Li Zhenhua,Xu Yanchun,Li Longfei,et al. Forecast of the height of water flowing fractured zone based on BP neural networks[J].Journal of Mining & Safety Engineering,2015, 32(6):905-910.[李振华,徐延春,李龙飞,等.基于BP神经网络的导水裂隙带高度预测[J].采矿与安全工程学报,2015,32(6):905-910.]
[7] Li Huimin,Li Zhenlei,He Rongjun,et al.Rock burst risk eva- luation based on particle swarm optimization and BP neural network[J].Journal of Mining & Safety Engineering,2014,31(2):203-207,231.[李慧民,李振雷,何荣军,等.基于粒子群算法和BP 神经网络的冲击危险性评估[J].采矿与安全工程学报,2014,31(2):203-207,231.]
[8] Wang Qian,Zhang Haixian.The depth of vehicle recognition based on neural network[J].Modern Computer,2015(35):61-64.[王茜,张海仙.基于深度神经网络的汽车车型识别[J].现代计算机(专业版),2015(35):61-64.]
[9] Fan Heng,Xu Jun,Deng Yong,et al.Behavior recognition of human based on deep learning[J].Geomatics and Information Science of Wuhan University,2016,4(4):492-497.[樊恒,徐俊,邓勇,等.基于深度学习的人体行为识别[J].武汉大学学报(信息科学版),2016,4(4):492-497.]
[10] Jia Y Q,Shelhamer E,Donahue F,et al.Caffe:Convolutional architecture for fast feature embedding[C]//Proceedings of the 22nd ACM International Conference on Multimedia,2014:675-678.
[11] Tompson J,Jain A,Lecun Y,et al.Joint training of a convolutional network and a graphical model for human pose estimation[J].Eprint Arxiv,2014:1799-1807.
[12]  Fathi A,Mori G.Action recognition by learning mid-level motion features[C]//IEEE International Conference on Com- puter Vision,2009:1-8.
[13] Krizhevsky A,Sutskever I,Hinton G E.ImageNet classi- fication with deep convolutional neural networks[C]// Advances in Neural Information Processing Systems,2012, 25:1-8.
[14] Chen Xianchang.Research and Application of Deep Learning Algorithm Based on Convolutional Neural Network[D].Hangzhou:Zhejiang Gongshang University,2013.[陈先昌.基于卷积神经网络的深度学习算法与应用研究[D].杭州:浙江工商大学,2013.]
[15] He K,Zhang X,Ren S,et al.Scalable,spatial pyramid pooling in deep convolutional network for visual recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,37(9):1904.
[16] Chen C C,Aggarwal J K.An adaptive background model initialization algorithm with object moving at different depths[C]//IEEE International Conference on Image Proces- sing,2006:1-7.
[17] Assael Y M,Wahlstrom N,Schon T B,et al.Data-efficient learning of feedback policies from image pixels using deep dynamical models[J].Computer Science,2015,48(28):1059-1064.
[18] LeCun Y,Bengio Y,Hinton G.Deep learning[J].Nature,2015,521:436-444.
[19] Bengio Y.Deep generative stochastic networks trainable by backprop[J].Department of Computer Science,Cornell Uni- versity,2013:5-7.
[20] LeCun Y,Bottou L,Bengio Y,et al.Gradient-based learning applied to document recognization[J].Proceedings of the IEEE,1998,86(11):2278-2324.
[21] Lian Xiaoyan,Deng Fang.CAPTCHA recognition based on image recognition and neural networks[J].Journal of Cen- tral South University(Science and Technology),2011,42(S):49-52.[连晓岩,邓方.基于图像识别和神经网络的验证码识别[J].中南大学学报(自然科学版),2011,42(增):49-52.]
[22] LeCun Y.Generalization and network design strategy[R].North-Holland:Connections in Perspective,1989:1-6.
[23]  LeCun  Y,Bengio Y,Szegedy G H,et al.Going deeper with convolution[J].Preprint arxiv,1409.4842,2014:5.
[24] Nair V,Hinton G E.Rectified linear units improve restricted boltzmann machines[J].Toronto:University of Toronto,2010: 3-6.
[25] Robinson A E,Hammon P S,De Sa V R.Explaining brightness illusions using spatial filtering and local response normalization[J].Vision Research,2007,47(12):1631-1644.
[26] Srivastava N,Hinton G,Krizhevsky A,et al.Dropout:A simple way to prevent neural networks from overfitting[J].The Journal of Mac

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