드라이브 ㆍ 컨트롤 (Journal of Drive and Control)

  • 제18권4호
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  • Pages.9-18
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  • 2021
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  • 2671-7972(pISSN)
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  • 2671-7980(eISSN)
유공압건설기계학회 (The Korean Society for Fluid Power and Construction Equipment)
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딥러닝 기반 장애물 인식을 위한 가상환경 및 데이터베이스 구축

Development of Virtual Simulator and Database for Deep Learning-based Object Detection

  • Lee, JaeIn (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Gwak, Gisung (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Kim, KyongSu (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Kang, WonYul (Institute of Vehicle Engineering) ;
  • Shin, DaeYoung (Institute of Industrial Technology) ;
  • Hwang, Sung-Ho (Department of Mechanical Engineering, Sungkyunkwan University)
  • 투고 : 2021.09.30
  • 심사 : 2021.10.29
  • 발행 : 2021.12.01
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초록

This study proposes a method for creating learning datasets to recognize obstacles using deep learning algorithms in automated construction machinery or an autonomous vehicle. Recently, many researchers and engineers have developed various recognition algorithms based on deep learning following an increase in computing power. In particular, the image classification technology and image segmentation technology represent deep learning recognition algorithms. They are used to identify obstacles that interfere with the driving situation of an autonomous vehicle. Therefore, various organizations and companies have started distributing open datasets, but there is a remote possibility that they will perfectly match the user's desired environment. In this study, we created an interface of the virtual simulator such that users can easily create their desired training dataset. In addition, the customized dataset was further advanced by using the RDBMS system, and the recognition rate was improved.

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과제정보

본 연구는 국토교통부/국토교통과학기술진흥원 교통물류연구사업의 연구비지원(21TLRP-C152478-03)과 2020년도 산업통상자원부 및 산업기술평가관리원 (KEIT) 연구비 지원에 의한 연구입니다.(과제번호 : 20010725)'

참고문헌

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