[1]胡云卿,潘文波,侯志超,等. 面向街景自动驾驶的DSC-MB-PSPNet语义分割技术研究[J].控制与信息技术,2020,(04):1.[doi:10.13889/j.issn.2096-5427.2020.01.001]
 HU Yunqing,PAN Wenbo,HOU Zhichao,et al. Research on Semantic Segmentation in Street Scenes Autonomous Driving with DSC-MB-PSPNet[J].High Power Converter Technology,2020,(04):1.[doi:10.13889/j.issn.2096-5427.2020.01.001]
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 面向街景自动驾驶的DSC-MB-PSPNet语义分割技术研究()
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《控制与信息技术》[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2020年04期
页码:
1
栏目:
出版日期:
2020-08-31

文章信息/Info

Title:
 Research on Semantic Segmentation in Street Scenes Autonomous Driving with DSC-MB-PSPNet
作者:
 胡云卿1潘文波1侯志超1金伟正2于 欢2
 (1. 中车株洲电力机车研究所有限公司,湖南 株洲 412001; 2. 武汉大学 电子信息学院,湖北 武汉 430079)
Author(s):
 HU Yunqing1 PAN Wenbo1 HOU Zhichao1 JIN Weizheng2 YU Huan2
 ( 1. CRRC Zhuzhou Institute Co., Ltd., Zhuzhou, Hunan 412001, China;2.School of Electronic Information, Wuhan University, Wuhan, Hubei 430072 , China )
关键词:
 自动驾驶语义分割深度可分离卷积深度学习抑制性交叉熵损失函数
Keywords:
 autonomous driving semantic segmentation deep separable convolution deep learning ICE (inhibitory cross entropy) loss function
分类号:
TN761.2;G642.423
DOI:
10.13889/j.issn.2096-5427.2020.01.001
文献标志码:
A
摘要:
 文章给出一种面向城市自动驾驶的轻量级可实时运行的语义分割模型;提出一种深度可分离卷积、多分支、金字塔池化尺度融合结构(简称DSC-MB-PSPNet),在保证了模型具有良好的表征能力的同时,可以做到实时运行;提出抑制性交叉熵损失函数以降低样本不平衡的影响,消除城市自动驾驶中各种类样本之间像素数量严重不均衡的问题,同时加入了多级损失函数计算方式,提升了训练的效率,使得模型更容易收敛。在公开数据集Cityscapes 及自搭建的数据集上训练,结果表明,在大分辨率输入的情况下,本文所提模型的精度在指标评测中居于前列,同时可以做到实时运行。
Abstract:
 It presented a lightweight real-time semantic segmentation model for city autonomous driving. A deep separable convolution, multi-branch and Pyramid scene parsing network fusion structure (DSC-MB-PSPNet) was proposed to ensure the model has better characterization ability and real-time operation, and inhibitory cross entropy loss function was presented to reduce the impact of sample imbalance and eliminate the serious imbalance of pixel number among samples in street scenes autonomous driving. At the same time, in order to improve the training efficiency and make the model easier to converge, the multi-stage loss function calculation method was added in the model. DSC-MB-PSPNet was evaluated on the Cityscapes and self-built datasets. The results show that under the condition of large resolution input, the proposed model achieves state-of-the-art performance and real-time.

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备注/Memo

备注/Memo:
 收稿日期:2019-09-08
作者简介:胡云卿(1984— ),男,博士,高级工程师,主要从事轨道交通领域智能控制技术研究。
基金项目:国家重点研发计划(2018YFB1201602)
更新日期/Last Update: 2020-07-31