A Lean Convolutional Neural Network for Vehicle Classification

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

  • Jonathan J. Sanchez-Castro
  • Julio C. Rodriguez-Quinonez
  • Luis R. Ramirez-Hernandez
  • Guillermo Galaviz
  • Daniel Hernandez-Balbuena
  • Gabriel Trujillo-Hernandez
  • Wendy Flores-Fuentes
  • Paolo Mercorelli
  • Wilmar Hernandez-Perdomo
  • Oleg Sergiyenko
  • Felix Fernando Gonzalez-Navarro

Image classification is an important task in machine vision, in which vehicle classification is used for different applications like traffic analysis, autonomous driving, security, among others. Recent studies made with Convolutional Neural Networks (CNN) have shown that these networks have surpassed older algorithms like Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) in terms of accuracy, speed, and resources management. Even though that CNN have better accuracy and speed they still are heavy in resource consumption on computers which makes them not suitable to deploy on an embedded platform. This paper proposes a lean CNN that has a smaller number of parameters and still maintaining the best accuracy possible on vehicle classification.

Original languageEnglish
Title of host publication2020 IEEE 29th International Symposium on Industrial Electronics (ISIE) : 17 - 19 June, 2020, Delft, Netherlands, Proceedings
Number of pages5
Place of PublicationPiscataway
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Publication date01.06.2020
Pages1365-1369
Article number9152274
ISBN (print)978-1-7281-5636-1
ISBN (electronic)978-1-7281-5635-4, 978-1-7281-5634-7
DOIs
Publication statusPublished - 01.06.2020
Event29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - TU Delft , Delft, Netherlands
Duration: 17.06.202019.06.2020
Conference number: 29
http://isie2020.org/

    Research areas

  • Artificial Intelligence, CNN, Vehicle Classification
  • Engineering