대한임베디드공학회논문지 (IEMEK Journal of Embedded Systems and Applications)

대한임베디드공학회 (Institute of Embedded Engineering of Korea)
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도로교통량 조사를 위한 12종 차종 분류 방법

Vehicle Type Classification Method for Road Traffic Surveys

  • 강미선 ;
  • 김찬호 ;
  • 김병근
  • Mi-Seon Kang (ETRI) ;
  • Chan-Ho Kim (Kyungpook National University) ;
  • Pyong-Kun Kim (ETRI)
  • 투고 : 2024.08.28
  • 심사 : 2024.09.24
  • 발행 : 2024.10.31
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초록

This paper proposes a novel method for effectively classifying 12 vehicle types required for road traffic surveys by utilizing deep learning techniques. In particular, it focuses on the trailer vehicle types, classified as types 8 to 12, which have been challenging in previous research due to data scarcity. A zero-shot learning approach, Grounding DINO, is employed to extract key features that can distinguish these trailer types, addressing the data imbalance issue. This method enables accurate classification of the underrepresented vehicle types, leading to efficient classification across all 12 types. To the best of the authors' knowledge, this is the first attempt to classify 12 vehicle types required for road traffic surveys using publicly available video data.

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과제정보

본 논문은 한국전자통신연구원 연구운영지원사업의 일환으로 수행되었음. [24ZD1120, 대경권 지역산업 기반 ICT 융합기술 고도화 지원사업]

참고문헌

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