Road Lane Line Detection Method Based on Improved YOLOv3 Algorithm

被引：0
|
作者：
Cui W.-L. [1 ]
Wang Y.-J. [1 ]
Kang S.-Q. [1 ]
Xie J.-B. [1 ]
Wang Q.-Y. [1 ]
Mikulovich V.I. [2 ]
机构：
[1] School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin
[2] School of Belarusian State University, Minsk
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2022年 / 48卷 / 06期
关键词：
Computer vision; Deep learning; K-means++; Lane detection; YOLOv3;
D O I：
10.16383/j.aas.c190178
中图分类号：
学科分类号：
摘要：
Aiming at the problem that the YOLOv3 algorithm has low accuracy, high probability of missed detection when detecting road lane lines, a road lane detection method for improving YOLOv3 network structure is proposed. At first, the method divides the image into multiple grids, and uses the K-means++ clustering algorithm to determine the number of target priori boxes and the corresponding value according to the inherent characteristics of the road lane line width and height. Then, according to the clustering result, the network anchor parameter is optimized to make the training network have certain pertinence in lane line detection. At last, the features extracted by the Darknet-53 are spliced, the network structure of the YOLOv3 algorithm is improved, and the GPU is used for multi-scale training to obtain the optimal weight model, thereby detecting the lane line target in the image and selecting the bounding box with the highest confidence to mark. Using the image information in the Caltech Lanes database for comparison experiments, the experimental results show that the improved YOLOv3 algorithm＇s mean average precision is 95% in road lane detection, the improved detection speed can be achieved 50 frame/s, which is 11% higher than the original algorithm and significantly higher than other lane detection methods. Copyright ©2022 Acta Automatica Sinica. All rights reserved.
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页码：1560 / 1568
页数：8
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