@inproceedings{7f07940ff40d43aeba3ceca0d25f10cb,
title = "Improvement of Object Detection Based on Faster R-CNN and YOLO",
abstract = "The development of artificial intelligence technology has been greatly assisted by object detection. The object detector like you-only-look-once (YOLO) v2 can detect an object in real-time and also with good accuracy. However, except for the lower computation cost and faster speed, the single-stage detector YOLO v2 is not as good as the two-stage detectors like Faster R-CNN in terms of accuracy; more improvement is needed to increase the accuracy. This paper uses the Kalman filter to fuse Faster R-CNN and YOLO v2 to obtain better detection accuracy. The results from Faster R-CNN are served as observation due to its better accuracy, while that from YOLO v2 as state variables. Experiment is carried out in video samples containing vehicle images. The results show that the fusion of the two algorithms by using the Kalman filter can provide better object detection.",
keywords = "Faster R-CNN, Kalman filter, object detection, YOLO v2",
author = "Jiayi Fan and Lee, {Jang Hyeon} and Jung, {In Su} and Lee, {Yong Keun}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021 ; Conference date: 27-06-2021 Through 30-06-2021",
year = "2021",
month = jun,
day = "27",
doi = "10.1109/ITC-CSCC52171.2021.9501480",
language = "English",
series = "2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021",
}