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Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study

  • Jorge E. Espinosa
  • Sergio A. VelastinEmail author
  • John W. Branch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10645)

Abstract

This paper presents a comparative study of two deep learning models used here for vehicle detection. Alex Net and Faster R-CNN are compared with the analysis of an urban video sequence. Several tests were carried to evaluate the quality of detections, failure rates and times employed to complete the detection task. The results allow to obtain important conclusions regarding the architectures and strategies used for implementing such network for the task of video detection, encouraging future research in this topic.

Keywords

Convolutional Neural Network Feature extraction Vehicle classification 

Notes

Acknowledgments

S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. The authors wish to thank Dr. Fei Yin for the code for metrics employed for evaluations. Finally, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research. The data and code used for this work is available upon request from the authors.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jorge E. Espinosa
    • 1
  • Sergio A. Velastin
    • 2
    • 3
    Email author
  • John W. Branch
    • 4
  1. 1.Facultad de IngenieríasPolitécnico Colombiano Jaime Isaza Cadavid – MedellínMedellínColombia
  2. 2.University Carlos III - MadridMadridSpain
  3. 3.Queen Mary University of LondonLondonUK
  4. 4.Facultad de MinasUniversidad Nacional de Colombia – Sede MedellínMedellínColombia

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