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A Road Segmentation Model Based on Mixture of the Convolutional Neural Network and the Transformer Network

Fenglei Xu#, Haokai Zhao#, Fuyuan Hu*, Mingfei Shen, Yifei Wu

Suzhou University of Science and Technology, Suzhou, 215009, China

* Corresponding Author: Fuyuan Hu. Email: email

(This article belongs to this Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)

Computer Modeling in Engineering & Sciences 2023, 135(2), 1559-1570.


Convolutional neural networks (CNN) based on U-shaped structures and skip connections play a pivotal role in various image segmentation tasks. Recently, Transformer starts to lead new trends in the image segmentation task. Transformer layer can construct the relationship between all pixels, and the two parties can complement each other well. On the basis of these characteristics, we try to combine Transformer pipeline and convolutional neural network pipeline to gain the advantages of both. The image is put into the U-shaped encoder-decoder architecture based on empirical combination of self-attention and convolution, in which skip connections are utilized for local-global semantic feature learning. At the same time, the image is also put into the convolutional neural network architecture. The final segmentation result will be formed by Mix block which combines both. The mixture model of the convolutional neural network and the Transformer network for road segmentation (MCTNet) can achieve effective segmentation results on KITTI dataset and Unstructured Road Scene (URS) dataset built by ourselves. Codes, self-built datasets and trainable models will be available on .


Cite This Article

Xu, F., Zhao, H., Hu, F., Shen, M., Wu, Y. (2023). A Road Segmentation Model Based on Mixture of the Convolutional Neural Network and the Transformer Network. CMES-Computer Modeling in Engineering & Sciences, 135(2), 1559–1570.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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