深度学习在驾驶员安全带检测中的应用

计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 182-187.

深度学习在驾驶员安全带检测中的应用

霍星¹, 费志伟², 赵峰², 邵堃³

合肥工业大学数学学院合肥230009¹;
合肥工业大学软件学院合肥230009²;
合肥工业大学计算机与信息学院合肥230009³

出版日期:2019-06-14 发布日期:2019-07-02
通讯作者: 霍星(1979-),女,博士,副教授,CCF会员,主要研究领域为机器学习、图形图像处理,E-mail:huoxing@hfut.edu.cn
作者简介:费志伟(1995-),男,硕士生,主要研究领域为深度学习、图像处理;赵峰(1964-),男,硕士,教授,高级工程师,主要研究领域为智能交通系统;邵堃(1967-),男,博士,副教授,CCF高级会员,主要研究领域为开放网络环境下的信任评估模型、需求工程、软件理论。
基金资助:
本文受国家自然科学基金面上项目(61872407,61572167),安徽省科技强警计划项目(1604d0802018),合肥工业大学2017年国家级大学生创新训练计划项目(201710359067),科技部国际合作项目(2015DFA11450),广东省省级科技计划项目(2016B010108002)资助。

Application of Deep Learning in Driver’s Safety Belt Detection

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

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

摘要/Abstract

摘要： 安全带是保障驾驶员安全最有效的措施之一,我国法律明文规定驾驶员驾驶车辆时必须佩带安全带。目前,驾驶过程中安全带佩带的识别以人工筛查为主。随着汽车数量的飞速增加,传统的检测方式已无法满足交通管理的需求,实现安全带检测的自动化处理已成为当前交通系统亟需解决的问题之一。文中设计了一种驾驶人是否佩带安全带的识别系统。首先,通过车牌与车窗位置之间的几何关系进行车窗粗定位;其次,利用霍夫变换检测车窗的上下沿,并利用积分投影变换检测车窗的左右边界,将检测到的图片对半划分,得到驾驶员的粗略位置;最后,基于加入空间变换层的深度卷积神经元网络方法进行安全带的识别分析。针对10000张不同卡口、不同时段实时采集的图片进行实验,结果表明该方法能有效地识别驾驶人是否按规定佩带安全带,且综合识别率相比现有方法有显著提高。

关键词: 安全带检测, 车窗边缘检测, 空间变换神经网络, 深度学习

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

中图分类号:

TP27

霍星, 费志伟, 赵峰, 邵堃. 深度学习在驾驶员安全带检测中的应用[J]. 计算机科学, 2019, 46(6A): 182-187. https://doi.org/

HUO Xing, FEI Zhi-wei, ZHAO Feng, SHAO Kun. Application of Deep Learning in Driver’s Safety Belt Detection[J]. Computer Science, 2019, 46(6A): 182-187. https://doi.org/

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