Four categories vehicle detection in hsuehshan tunnel via single shot multibox detector

Chun Ming Tsai*, Tawei Shou, Jun-Wei Hsieh, Kuang Hsuan Chen

*Corresponding author for this work

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

Abstract

Taiwan has many vehicles and as a result many traffic problems. In particular, during the Spring Festival and the holidays, Hsuehshan Tunnel (HST) between Yilan and Taipei is always a traffic jam. To solve this problem, intelligent transportation system (ITS) is necessary, and accurate vehicle detection (VD) is the first stage for ITS. In order to detect vehicles in HST, three training methods based on single shot multibox detector (SSD) are presented to detect four categories of vehicle in the Tunnel. The experimental results demonstrated that the presented three training methods, which only used 1000 training frames, can detect and categorize more vehicles than the pre-trained SSD model which used a large training dataset. Specifically, the SSD trained by our collected data set and data augmentation has the highest detection rates for sedan, van, bus, and truck - 93.6%, 90.9%, 100%, and 100%, respectively.

Original languageEnglish
Title of host publicationProceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages113-118
Number of pages6
ISBN (Electronic)9781728128207
DOIs
StatePublished - Aug 2019
Event12th International Conference on Ubi-Media Computing, Ubi-Media 2019 - Bali, Indonesia
Duration: 6 Aug 20199 Aug 2019

Publication series

NameProceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019

Conference

Conference12th International Conference on Ubi-Media Computing, Ubi-Media 2019
CountryIndonesia
CityBali
Period6/08/199/08/19

Keywords

  • Deep learning
  • Four categories vehicle detection
  • Hsuehshan Tunnel
  • Intelligent transportation system
  • Single shot multibox detector

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