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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

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