Intelligent Vehicle Automatic Identification System Based on YOLOv4 and ViSLAM
- Authors
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Chenzhi Nie
Shanghai University of Engineering Sciences
Author
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Wei Lin
Shanghai University of Engineering Sciences
Author
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Xiuwen Zheng
Shanghai University of Engineering Sciences
Author
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- Keywords:
- Array, Array, Array
- Abstract
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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.
- Author Biographies
- References
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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.
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- Published
- 2023-05-09
- Section
- Journal Articles
- License
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Copyright (c) 2023 Chenzhi Nie, Wei Lin, Xiuwen Zheng
This work is licensed under a Creative Commons Attribution 4.0 International License.
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