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Comparison of CNN and YOLO for Object Detection  

Lee, Yong-Hwan (Dept. of Digital Contents, Wonkwang University)
Kim, Youngseop (Dept. of Electronics and Electrical Engineering, Dankook University)
Publication Information
Journal of the Semiconductor & Display Technology / v.19, no.1, 2020 , pp. 85-92 More about this Journal
Abstract
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.
Keywords
Object Detection; CNN (Convolutional Neural Networks); YOLO (You Only Look Once); Computer Vision;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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