Acceleration-Based Deep Learning Method for Vehicle Monitoring

Yanjie Zhu, Hidehiko Sekiya, Takayuki Okatani, Ikumasa Yoshida, Shuichi Hirano

研究成果: Article査読

抄録

An automatic vehicle monitoring system can provide supports not only for intelligent transportation systems, but also for bridge weigh-in-motion (BWIM) systems, which use structural response to identify vehicle weights. In this paper, we provide a vehicle monitoring solution for acceleration-based BWIM system, utilizing deep learning and wavelet transform methods. The monitoring task is divided into three subtasks, including valid sequence detection, valid axle location, and driving lane identification. In first procedure, a shallow convolutional neural network is trained using time-frequency spectrograms to discover valuable time series. After that, an adaptive wavelet transform method is employed to locate axles from each valid sequence. Finally, the driving lane can be determined by cross-comparing vibration responses. Comparing with solutions based on all-in-one deep networks, the proposed method is computationally efficient and has improved generalization capability owing to the three-step division of the task. Evaluation is conducted on a multi-lane highway bridge located in Tokyo. Results show that 97% of vehicles can be identified correctly. For all recognized vehicles, the accuracy of driving lane detection is 100%.

本文言語English
論文番号9437183
ページ(範囲)17154-17161
ページ数8
ジャーナルIEEE Sensors Journal
21
15
DOI
出版ステータスPublished - 2021 8 1

ASJC Scopus subject areas

  • 器械工学
  • 電子工学および電気工学

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