Research Article
A novel dilated convolutional neural network model for road scene segmentation
@ARTICLE{10.4108/eai.27-1-2022.173164, author={Yachao Zhang and Yuxia Yuan}, title={A novel dilated convolutional neural network model for road scene segmentation}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={9}, number={4}, publisher={EAI}, journal_a={SIS}, year={2022}, month={1}, keywords={road scene segmentation, dilated convolutional neural network, scene understanding}, doi={10.4108/eai.27-1-2022.173164} }- Yachao Zhang
Yuxia Yuan
Year: 2022
A novel dilated convolutional neural network model for road scene segmentation
SIS
EAI
DOI: 10.4108/eai.27-1-2022.173164
Abstract
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173784.
Road scene understanding is one of the important modules in the field of autonomous driving. It can provide more information about roads and play an important role in building high-precision maps and real-time planning. Among them, semantic segmentation can assign category information to each pixel of image, which is the most commonly used method in automatic driving scene understanding. However, most commonly used semantic segmentation algorithms cannot achieve a good balance between speed and precision. In this paper, a road scene segmentation model based on dilated convolutional neural network is constructed. The model consists of a front-end module and a context module. The front- end module is an improved structure of VGG-16 fused dilated convolution, and the context module is a cascade of dilated convolution layers with different expansion coefficients, which is trained by a two-stage training method. The network proposed in this paper can run in real time and ensure the accuracy to meet the requirements of practical applications, and has been verified and analyzed on Cityscapes data set.
Copyright © 2022 Yachao Zhang et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
