Comparison of CNN and YOLO for Object Detection

객체 검출을 위한 CNN과 YOLO 성능 비교 실험

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


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.



Supported by : 원광대학교

본 논문은 2019학년도 원광대학교의 교비 지원에 의해 수행됨.


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