Research on lane identification based on deep learning

Article type: Research Article

Authors: Zhang, Chuanwei^* | Qin, Peilin | Yu, Zhengyang

Affiliations: Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, Shaanxi, China

Correspondence: [*] Corresponding author: Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, Shaanxi, China. E-mail: 769365808@qq.com.

Abstract: In order to meet the real-time and robustness requirement of driverless cars driving on highway, this paper proposed a lane line identification method based on the Deep Learning. This method first built a lane line image library and then input the pictures of the lane line image for denoising and normalized processing. Secondly, the Lenet – 5 network model was used for classification and recognition, with a recognition rate of 99.4%, and the lane line type was displayed through GUI interface. Finally, this method was compared with the support vector machine and BP neural network, and the results effectively verified that the method can satisfy the requirement of real-time and accuracy of lane line identification.

Keywords: Image processing, lane line identification, convolutional neural network, deep learning

DOI: 10.3233/JCM-193593

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 20, no. 1, pp. 3-11, 2020