基于深度卷积神经网络的场景自适应道路分割算法

王海; 蔡英凤; 贾允毅; 陈龙; 江浩斌

doi:10.11999/JEIT160329

基于深度卷积神经网络的场景自适应道路分割算法

doi: 10.11999/JEIT160329

1.
江苏大学汽车与交通工程学院镇江 212013
2.
江苏大学汽车工程研究院镇江 212013
3.
克莱姆森大学汽车工程系美国南卡罗拉纳州 29634

基金项目:

国家自然科学基金(U1564201, 61601203, 61573171, 61403172)，中国博士后基金(2014M561592, 2015T80511)，江苏省重点研发计划(BE2016149)，江苏省自然科学基金(BK20140555)，江苏省六大人才高峰项目(2015-JXQC-012, 2014-DZXX-040)

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出版历程
- 收稿日期: 2016-04-05
- 修回日期: 2016-08-22
- 刊出日期: 2017-02-19

Scene Adaptive Road Segmentation Algorithm Based on Deep Convolutional Neural Network

1.
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
2.
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
3.
Department of Automotive Engineering, Clemson University, South Carolina 29634, USA

Funds:

The National Natural Science Foundation of China (U1564201, 61601203, 61573171, 61403172), The China Postdoctoral Science Foundation (2014M561592, 2015T80511), The Key Research and Development Program of Jiangsu Province (BE2016149), The Natural Science Foundation of Jiangsu Province (BK20140555), The Six Talent Peaks Project of Jiangsu Province (2015-JXQC-012, 2014-DZXX-040)

摘要

摘要: 现有基于机器学习的道路分割方法存在当训练样本和目标场景样本分布不匹配时检测效果下降显著的缺陷。针对该问题，该文提出一种基于深度卷积网络和自编码器的场景自适应道路分割算法。首先，采用较为经典的基于慢特征分析(SFA)和GentleBoost的方法，实现了带标签置信度样本的在线选取；其次，利用深度卷积神经网络(DCNN)深度结构的特征自动抽取能力，辅以特征自编码器对源-目标场景下特征相似度度量，提出了一种采用复合深度结构的场景自适应分类器模型并设计了训练方法。在KITTI测试库的测试结果表明，所提算法较现有非场景自适应道路分割算法具有较大的优越性，在检测率上平均提升约4.5%。
- 道路分割 /
- 场景自适应 /
- 深度卷积神经网络 /
- 复合深度结构 /
- 自编码器
Abstract: The existed machine learning based road segmentation algorithms maintain obvious shortage that the detection effect decreases dramatically when the distribution of training samples and the scene target samples does not match. Focusing on this issue, a scene adaptive road segmentation algorithm based on Deep Convolutional Neural Network (DCNN) and auto encoder is proposed. Firstly, classic Slow Feature Analysis (SFA) and Gentle Boost based method is used to generate online samples whose label contain confidence value. After that, using the automatic feature extraction ability of DCNN and performing source-target scene feature similarity calculation with deep auto-encoder, a composite deep structure based scene adaptive classifier and its training method are designed. The experiment on KITTI dataset demonstrates that the proposed method outperforms the existed machine learning based road segmentation algorithms which upgrades the detection rate on average of around 4.5%.
- Road segmentation /
- Scene adaptive /
- Deep Convolutional Neural Network (DCNN) /
- Composite deep structure /
- Auto-encoder

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