物联网学报 ›› 2020, Vol. 4 ›› Issue (3): 120-125.doi: 10.11959/j.issn.2096-3750.2020.00175

• 专题:智慧交通物联网 • 上一篇    

基于SqueezeNet卷积神经网络的车辆检测

魏泽发1(),崔华2   

  1. 1 长安大学教育技术与网络中心,陕西 西安 710064
    2 长安大学信息工程学院,陕西 西安 710064
  • 修回日期:2020-07-06 出版日期:2020-09-30 发布日期:2020-09-07
  • 作者简介:魏泽发(1992- ),男,河北衡水人,长安大学助理工程师,主要研究方向为智能交通、深度学习、图像处理等|崔华(1977- ),女,陕西西安人,博士,长安大学教授,主要研究方向为图像分析、数据挖掘、机器学习、深度学习及其在智能交通中的应用研究等
  • 基金资助:
    陕西省科技厅重点研发计划(2018ZDXM-GY-047)

Vehicle detection based on SqueezeNet convolutional neural network

Zefa WEI1(),Hua CUI2   

  1. 1 Educational Technology and Network Center,Chang’an University,Xi’an 710064,China
    2 School of Information Engineering,Chang’an University,Xi’an 710064,China
  • Revised:2020-07-06 Online:2020-09-30 Published:2020-09-07
  • Supported by:
    The Key R&D Plan of Shaanxi Provincial Science and Technology Department(2018ZDXM-GY-047)

摘要:

在智能交通系统中,针对车辆目标检测算法可移植性不高、检测速度较慢等问题,提出了一种基于SqueezeNet卷积神经网络的车辆检测方法。通过融合SqueezeNet与SSD(single shot multibox detector)算法的车辆检测方法,在UA-DETRAC数据集上进行训练,实现了车辆目标的快速检测,提升了模型的可移植性,缩短了单帧检测时间。实验结果表明,所提模型在保证准确率的同时,模型单帧检测时间可达22.3 ms,模型大小为16.8 MB,相较于原SSD算法,模型大小减少了约8/9。

关键词: 智能交通, 卷积神经网络, SqueezeNet, 车辆检测

Abstract:

In the intelligent transportation system,aiming at the problem of low portability and speed of detection in vehicle target detection algorithm,a vehicle detection method based on SqueezeNet convolutional neural network was proposed.In order to realize the rapid detection of vehicle targets,improve the portability and shorten the detection time of the single frame,the model was trained on the UA-DETRAC dataset by fusing the SqueezeNet with the single shot multibox detector (SSD) algorithm.The experimental results showed that the time of the single frame detection could reach 22.3 ms and the model size was 16.8 MB.Compared with the original SSD algorithm,the model size was reduced by about 8/9.At the same time,the accuracy of the proposed model was guaranteed.

Key words: intelligent transportation, convolutional neural network, SqueezeNet, vehicle detection

中图分类号: