| Issue |
SHS Web Conf.
Volume 140, 2022
2022 International Conference on Information Technology in Education and Management Engineering (ITEME2022)
|
|
|---|---|---|
| Article Number | 01018 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/shsconf/202214001018 | |
| Published online | 25 May 2022 | |
Research on road vehicle and lane line detection based on improved YOLOv4-tiny algorithm
1
School of Mechanical Engineering, Hubei Institute of Automotive Technology, Shiyan, China
2
School of Automotive Engineers, Hubei Institute of Automotive Technology, Shiyan, China
* Corresponding author: 8784145@163.com
In order to improve the speed of road vehicle and lane line detection, a road vehicle and lane line detection method with improved YOLOv4-tiny algorithm is proposed by using K-means++ clustering algorithm instead of the original K-means algorithm. Using the Mosaic data enhancement method, four images are randomly deflated and then stitched into one image to enrich the detection target background. The optimal weight values are derived by multi-scale training using the GPU through image feature extraction of the BDD10K dataset to achieve the detection of vehicle and lane line targets in the images. The results show that the improved YOLOv4-tiny algorithm achieves a detection speed of 134 FPS and an average accuracy of 77.84% mAP in highway lane line detection. After comparison, the detection speed of the improved algorithm is significantly improved, effectively improving the efficiency of highway vehicle and lane line detection.
Key words: Clustering algorithm / Improved YOLOv4-tiny algorithm / Lane line detection / Deep learning
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.