Stop line detection and distance measurement for road intersection based on deep learning neural network

Guan Ting Lin, Patrisia Sherryl Santoso, Che Tsung Lin, Chia Chi Tsai, Jiun In Guo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, we introduce Boost-CNN, a robust stop-line detector that can detect objects (stop line) with competitive tradeoff between speed and accuracy. Boost-CNN consists of an AdaBoost classifier and a CNN. The former is our region proposal generator and it is further combined with the later to be a stop-line detector. In addition, an automatic hard mining method is proposed to reduce the number of false alarm. Our proposed detector achieves 91.5% in accuracy and has 100 FPS performance in test time (performed on NVIDIA DIGITS DevBox and Titan X GPU).

Original languageEnglish
Title of host publicationProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages692-695
Number of pages4
ISBN (Electronic)9781538615423
DOIs
Publication statusPublished - 2018 Feb 5
Event9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 - Kuala Lumpur, Malaysia
Duration: 2017 Dec 122017 Dec 15

Publication series

NameProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Volume2018-February

Other

Other9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Country/TerritoryMalaysia
CityKuala Lumpur
Period17-12-1217-12-15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Information Systems
  • Signal Processing

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