Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Yolo based Light Source Object Detection for Traffic Image Big Data Processing

교통 영상 빅데이터 처리를 위한 Yolo 기반 광원 객체 탐지

  • Kang, Ji-Soo (Department of Computer Science, Kyonggi University) ;
  • Shim, Se-Eun (Division of Computer Science and Engineering, Kyonggi University) ;
  • Jo, Sun-Moon (Department of Computer Information Technology Education, Paichai University) ;
  • Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
  • 강지수 (경기대학교 컴퓨터과학과) ;
  • 심세은 (경기대학교 컴퓨터공학부) ;
  • 조선문 (배재대학교 IT교육학과) ;
  • 정경용 (경기대학교 컴퓨터공학부)
  • Received : 2020.07.02
  • Accepted : 2020.08.20
  • Published : 2020.08.28
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Abstract

As interest in traffic safety increases, research on autonomous driving, which reduces the incidence of traffic accidents, is increased. Object recognition and detection are essential for autonomous driving. Therefore, research on object recognition and detection through traffic image big data is being actively conducted to determine the road conditions. However, because most existing studies use only daytime data, it is difficult to recognize objects on night roads. Particularly, in the case of a light source object, it is difficult to use the features of the daytime as it is due to light smudging and whitening. Therefore, this study proposes Yolo based light source object detection for traffic image big data processing. The proposed method performs image processing by applying color model transitions to night traffic image. The object group is determined by extracting the characteristics of the object through image processing. It is possible to increase the recognition rate of light source object detection on a night road through a deep learning model using candidate group data.

교통안전에 대한 관심이 높아짐에 따라 교통사고의 발생률을 줄이는 자율 주행에 대한 연구가 지속적으로 진행되고 있다. 객체의 인식과 탐지는 자율 주행을 위한 필수적인 요소이다. 때문에 도로 상황을 판단하기 위하여 교통 영상 빅데이터에서 객체 인식 및 탐지에 대한 연구가 활발히 진행 중이다. 하지만 기존 연구들은 대부분 주간 데이터만 사용하기 때문에 야간 도로에서 객체 인식이 어렵다. 특히 광원 객체의 경우 빛 번짐과 백화 현상으로 인해 주간의 특징을 그대로 사용하기 어렵다. 따라서 본 연구에서는 교통 영상 빅데이터 처리를 위한 Yolo 기반 광원 객체 탐지를 제안한다. 제안하는 방법은 야간 교통 영상을 대상으로 색상 모델 변화를 적용하여 이미지 처리를 수행한다. 이미지 처리를 통해서 객체의 특징을 추출하여 객체의 후보군을 결정한다. 후보군 데이터를 활용하여 딥러닝 모델을 통해 야간 도로에서 광원 객체 탐지의 인식률을 높이는 것이 가능하다.

Keywords

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