Ford motor side-view recognition system based on wavelet entropy and back propagation neural network and levenberg-marquardt algorithm

Wen Juan Jia, Shuihua Wang*, Huimin Lu, Ying Shao, Elizabeth Lee, Yu Dong Zhang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)

Abstract

(Aim) Automatic identification of the car manufacturer in the side-view position can be used for the intelligent traffic monitoring system. Currently, the side-view car recognition did not attract too much attention. (Method) We proposed a novel Ford Motor recognition system. We first captured the car image from the side view. Second, we used wavelet entropy to extract texture features. Third, we employed a back propagation neural network (BPNN) as the classifier. Finally, we employed the Levenberg-Marquardt algorithm to train the classifier. In the experiment, we utilized the 3 × 3-fold cross validation. (Result) This method achieved an overall accuracy of 80% in detecting Ford motors. (Conclusion) This method can detect Ford Motors from the side view effectively. In the future, it may also be used to detect cars of other brands.

Original languageEnglish
Title of host publicationParallel Architecture, Algorithm and Programming - 8th International Symposium, PAAP 2017, Proceedings
EditorsHong Shen, Guoliang Chen, Mingrui Chen
PublisherSpringer Verlag
Pages3-12
Number of pages10
ISBN (Print)9789811064418
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event8th International Symposium on Parallel Architectures, Algorithms, and Programming, PAAP 2017 - Haikou, China
Duration: 17 Jun 201718 Jun 2017

Publication series

NameCommunications in Computer and Information Science
Volume729
ISSN (Print)1865-0929

Conference

Conference8th International Symposium on Parallel Architectures, Algorithms, and Programming, PAAP 2017
Country/TerritoryChina
CityHaikou
Period17/06/1718/06/17

Keywords

  • Back propagation neural network
  • Cross validation
  • Ford motor
  • Levenberg-Marquardt algorithm
  • Pattern recognition
  • Recognition
  • Wavelet entropy

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