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Ford Vehicle Classification Based on Extreme Learning Machine Optimized by Bat Algorithm

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Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health (CyberDI 2019, CyberLife 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1138))

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Abstract

The application of automobile identification in life is more and more extensive, so research on related technologies is receiving widespread attention. This article focuses on research on Ford vehicle identification, the theoretical method of identification is proposed and its effectiveness is verified in experiments. We first obtain the side-view image of the Ford car. Secondly, we use gray level co-occurrence to extract the feature of Ford car. Third, we use extreme learning machine as the classifier. Finally, we use bat algorithm to optimize the algorithm, and employ 10-fold cross-validation to ensure the validity of the data. The results of the research indicate that in the same kind of research, the method we employ has the highest accuracy (84.92 ± 0.64%).

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Authors and Affiliations

  1. School of Education Science, Nanjing Normal University, Nanjing, 210023, Jiangsu, China

    Yile Zhao & Zhihai Lu

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  1. Yile Zhao
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  2. Zhihai Lu
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Correspondence to Yile Zhao .

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Editors and Affiliations

  1. University of Science and Technology, Beijing, China

    Huansheng Ning

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Zhao, Y., Lu, Z. (2019). Ford Vehicle Classification Based on Extreme Learning Machine Optimized by Bat Algorithm. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_26

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  • DOI: https://doi.org/10.1007/978-981-15-1925-3_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1924-6

  • Online ISBN: 978-981-15-1925-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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