객체 검출을 위한 CNN과 YOLO 성능 비교 실험

Comparison of CNN and YOLO for Object Detection

  • 이용환 (원광대학교 디지털콘텐츠공학과) ;
  • 김영섭 (단국대학교 전자전기공학부)
  • Lee, Yong-Hwan (Dept. of Digital Contents, Wonkwang University) ;
  • Kim, Youngseop (Dept. of Electronics and Electrical Engineering, Dankook University)
  • 투고 : 2020.03.15
  • 심사 : 2020.03.23
  • 발행 : 2020.03.31

초록

Object detection plays a critical role in the field of computer vision, and various researches have rapidly increased along with applying convolutional neural network and its modified structures since 2012. There are representative object detection algorithms, which are convolutional neural networks and YOLO. This paper presents two representative algorithm series, based on CNN and YOLO which solves the problem of CNN bounding box. We compare the performance of algorithm series in terms of accuracy, speed and cost. Compared with the latest advanced solution, YOLO v3 achieves a good trade-off between speed and accuracy.

키워드

과제정보

연구 과제 주관 기관 : 원광대학교

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