Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 182-187.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Application of Deep Learning in Driver’s Safety Belt Detection

HUO Xing1, FEI Zhi-wei2, ZHAO Feng2, SHAO Kun3   

  1. School of Mathematics,Hefei University of Technology,Hefei 230009,China1;
    School of Software,Hefei University of Technology,Hefei 230009,China2;
    School of Computer Science & Information Engineering,Hefei University of Technology,Hefei 230009,China3
  • Online:2019-06-14 Published:2019-07-02

Abstract: Seat belts are one of the most effective measures to protect safety of drivers which the law stipulates that drivers must wear seat belts when driving the vehicle.At present,the identification of seat belt during driving is mainly based on manual screening.However,the traditional detection methods can not meet the needs of traffic management as the rapid increase of the number of vehicles.And the automatic processing of seat belt detection has become one of the urgent problems in the current traffic system.In this paper,a recognition system for seat belts of drivers is designed.First,the vehicle window is roughly positioned by the geometric relationship between the license plate and the window.Second,Hough transform is used to detect the upper and lower edges of the window and the integral projection transformation is used to detect the left and right borders of the window.The detected pictures will be cut into half to get the driver rough position.Finally,the seat belt identification analysis based on deep convolutional neural network is conducted which adds spatial transform layer.Experiments are carried out on different bayonet and different time periods for 10000 pictures.The experimental results show that the proposed method can effectively identify whether the driver wears the seat belt according to the regulations,and the comprehensive recognition rate is significantly improved compared with the existing method.

Key words: Car window edge detection, Deeping learning, Seat belt detection, Spatial transform networks

CLC Number: 

  • TP27
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