Convolution neural network with selective multi-stage feature fusion: Case study on vehicle rear detection

Won Jae Lee, Dong W. Kim, Tae Koo Kang, Myo Taeg Lim

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Vision-based vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain characteristic vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithmusing these features to implement a vehicle detector. The experimental results verified that the proposed vehicle detection framework exhibited better performance than previous frameworks.

Original languageEnglish
Article number2468
JournalApplied Sciences (Switzerland)
Issue number12
Publication statusPublished - 2018 Dec 3

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by theMinistry of Education (Grant Nos. NRF-2016R1D1A1B01016071 and NRF-2017R1D1A1B03031467).

Publisher Copyright:
© 2018 by the authors.


  • AdaBoost
  • Convolutional neural network
  • Feature extraction
  • Vehicle detection

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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