Intelligent Vehicle Automatic Identification System Based on YOLOv4 and ViSLAM
DOI:
https://doi.org/10.31686/ijier.vol11.iss5.4118Keywords:
Intelligent Vehicle, YOLOv4, CNN modelAbstract
In this paper, we use intelligent vehicles as the platform and use convolutional neural networks for lane recognition and classification during driving. For the recognition of landmarks, we use YOLOv4, a popular YOLO series algorithm, as the model for recognition. At the same time, we study and explore intelligent vehicle mapping and positioning technology based on the SLAM framework in a laboratory working environment with weak signals.
References
Zhou Feiyan, Jin Linpeng, Dong Jun. A review of convolutional neural networks. Journal of Computer Science, 2017,40 ( 06 ) : 1229-1251.
Research on target detection of unmanned driving scene based on YOLO algorithm. Southwest University, 2021.DOI : 10.27684 / d.cnki.gxndx.2021.003227.
Cheng Ze, Lin Fusheng, Jin Chao, et al. Fatigue driving detection based on lightweight convolutional neural network. Journal of Chongqing University of Technology (Natural Science), 2022,36 (02):142-150.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Chenzhi Nie, Wei Lin, Xiuwen Zheng
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyrights for articles published in IJIER journals are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author for more visit Copyright & License.
