Scene Adaptive Road Segmentation Algorithm Based on Deep Convolutional Neural Network

WANG Hai; CAI Yingfeng; JIA Yunyi; CHEN Long; JIANG Haobin

doi:10.11999/JEIT160329

Volume 39 Issue 2

Feb. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(2): 263-269

WANG Hai, CAI Yingfeng, JIA Yunyi, CHEN Long, JIANG Haobin. Scene Adaptive Road Segmentation Algorithm Based on Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2017, 39(2): 263-269. doi: 10.11999/JEIT160329

Citation:

WANG Hai, CAI Yingfeng, JIA Yunyi, CHEN Long, JIANG Haobin. Scene Adaptive Road Segmentation Algorithm Based on Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2017, 39(2): 263-269. doi: 10.11999/JEIT160329

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Scene Adaptive Road Segmentation Algorithm Based on Deep Convolutional Neural Network

doi: 10.11999/JEIT160329

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)

Received Date: 2016-04-05
Rev Recd Date: 2016-08-22
Publish Date: 2017-02-19

Abstract

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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References(21)

References

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