Study on traffic scene semantic segmentation method based on convolutional neural network

Linhui LI; Bo QIAN; Jing LIAN; Weina ZHENG; Yafu ZHOU

doi:10.11959/j.issn.1000-436x.2018053

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Study on traffic scene semantic segmentation method based on convolutional neural network

Papers | 更新时间：2024-06-05

- Study on traffic scene semantic segmentation method based on convolutional neural network
- Journal on Communications Vol. 39, Issue 4, Pages: 123-130(2018)
- 作者机构：
  
  大连理工大学工业装备结构分析国家重点实验室运载工程与力学学部汽车工程学院，辽宁大连 116024
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(51775082);The National Natural Science Foundation of China(61473057);The National Natural Science Foundation of China(61203171)
- DOI：10.11959/j.issn.1000-436x.2018053
  CLC： U495，TP391.4
- Online First：2018-04，
  
  Published：25 April 2018
- 稿件说明：
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Linhui LI, Bo QIAN, Jing LIAN, et al. Study on traffic scene semantic segmentation method based on convolutional neural network[J]. Journal on Communications, 2018, 39(4): 123-130.
DOI：

Linhui LI, Bo QIAN, Jing LIAN, et al. Study on traffic scene semantic segmentation method based on convolutional neural network[J]. Journal on Communications, 2018, 39(4): 123-130. DOI： 10.11959/j.issn.1000-436x.2018053.

摘要

为提高交通场景的语义分割精度，提出一种基于 RGB-D 图像和卷积神经网络的分割方法。首先，基于半全局立体匹配算法获取视差图D，并将其与RGB图像融合成四通道RGB-D图像，以建立样本库；其次，对于2种不同结构的卷积神经网络，分别采用2种不同的学习率调整策略对网络进行训练；最后，对训练得到的网络进行测试及对比分析。实验结果表明，基于RGB-D图像的交通场景语义分割算法得到的分割精度高于基于RGB图像的分割算法。

Abstract

In order to improve the semantic segmentation accuracy of traffic scene

a segmentation method was proposed based on RGB-D image and convolutional neural network.Firstly

on the basis of semi-global stereo matching algorithm

the disparity map was obtained

and the sample library was established by fusing the disparity map D and RGB image into the four-channel RGB-D image.Then

with two different structures

the networks were trained by using two different learning rate adjustment strategy respectively.Finally

the traffic scene semantic segmentation test was carried out with RGB-D image as the input

and the results were compared with the segmentation method based on RGB image.The experimental results show that the proposed traffic scene segmentation algorithm based on RGB-D image can achieve higher semantic segmentation accuracy than that based on RGB image.

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references

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