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Academic Journal of Computing & Information Science, 2022, 5(11); doi: 10.25236/AJCIS.2022.051114.

Pavement Recognition Based on Multiple Deep Learning


Fei Xie1, Li Yang1,2, Hailong Duan1,2

Corresponding Author:
Fei Xie

1Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education, Tianjing, China

2School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjing, China


In order to meet the development of The Times and enhance the intelligence of new energy vehicles, and solve the problem that the traditional image processing is too demanding for the feature quantity and the recognition effect is poor, a road recognition research based on deep learning is designed. Four deep learning networks (VGGNet, ResNet, GoogLeNet and AlexNet) were used in the study. A large number of images of different road surfaces were collected as experimental data, and the data were trained by the deep learning network. By comparing the results of four kinds of networks, the network which can be used in intelligent vehicles and has better recognition rate is finally analyzed. The research achieved the expected effect and laid the foundation for the subsequent design.


Deep learning, road recognition, Convolutional neural networks, Smart cars

Cite This Paper

Fei Xie, Li Yang, Hailong Duan. Pavement Recognition Based on Multiple Deep Learning. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 11: 90-96. https://doi.org/10.25236/AJCIS.2022.051114.


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