Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (9): 26-31.doi: 10.16180/j.cnki.issn1007-7820.2019.09.006

Previous Articles     Next Articles

Recognition of Underground Miners’ Rule-Violated Behavior Based on Safety Helmet Detection

TONG Zeyou1,2,3,FENG Shimin1,2,HOU Xiaoqing1,2,3,DING Enjie1,2   

  1. 1.Internet of Things (Perception Mine) Research Center,China University of Mining and Technology,Xuzhou 221000,China
    2.The National and Local Joint Engineering Laboratory of Internet Application Technology on Mine,Xuzhou 221000,China
    3.Institute of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221000,China
  • Received:2018-09-06 Online:2019-09-15 Published:2019-09-19
  • Supported by:
    2017 National Key R&D Projects(2017YFC0804401)


In order to solve the problem of the miner's helmet-wearing recognition, this paper proposed a method of detecting the safety helmet based rule-violated activity. It was based on the face recognition and helmet detection. Firstly, detect the human faced in the picture and identify them, and then detect whether the person wears a safety helmet or not with the Convolutional Neural Network approach. This paper compared the depth learning method with the traditional method in different conditions in the experiments.The experimental results showed that the robustness of the depth learning based safety helmet detection method was stronger than the traditional method. The recognition rate of the depth learning method was higher than that of the traditional approach under different conditions. The deep learning method achieved an average recognition rate of 97% and the average running time was less than 1/7 of that of the traditional approach.

Key words: safety helmet detection, identity recognition, rule-violated activity, deep learning, accuracy and efficiency, average time

CLC Number: 

  • TN399