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Erschienen in:
A Markov Approximation Algorithm for Computation Offloading and Resource Scheduling in Mobile Edge Computing Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

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

verfasst von : Yile Zhao, Zhihai Lu

Erschienen in: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health

Verlag: Springer Singapore

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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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Vorheriges Kapitel Semi-automated Development of a Dataset for Baseball Pitch Type Recognition
Nächstes Kapitel Design and Implementation of a Wearable System for Information Monitoring
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Metadaten
Titel
Ford Vehicle Classification Based on Extreme Learning Machine Optimized by Bat Algorithm
verfasst von
Yile Zhao
Zhihai Lu
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health

Print ISBN: 978-981-15-1924-6

Electronic ISBN: 978-981-15-1925-3

Copyright-Jahr: 2019

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

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