@article{8de4c24c3e2145ff92354104db8dd504,
title = "Multi-scale dilated convolution network based depth estimation in intelligent transportation systems",
abstract = "Vision based depth estimation plays a significant role in Intelligent Transportation Systems (ITS) because of its low cost and high efficiency, which can be used to analyze driving environment, improve driving safety, etc. Although recently proposed approaches abandon time consuming pre-processing or post-processing steps and achieve an end-to-end prediction manner, fine details may be lost through max-pooling based encode modules. To tackle this problem, we propose Multi-Scale Dilated Convolution Network (MSDC-Net), a dilated convolution based deep network. For the feature encoding and decoding part, dilated layers maintain the scale of original image and reduce lost details. After that, a pyramid dilated feature extraction module is added to integrate the knowledge learned through forward steps with different receptive fields. The proposed approach is evaluated on KITTI dataset, and achieves a state-of-the-art result on the dataset.",
keywords = "Depth estimation, ResNet, dilated network, intelligent transportation systems (ITS), multi-scale dilated module",
author = "Yanling Tian and Qieshi Zhang and Ziliang Ren and Fuxiang Wu and Pengyi Hao and Jinglu Hu",
note = "Funding Information: This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1308000, in part by the National Natural Science Funds of China under Grant U1913202, Grant U1813205, Grant U1713213, Grant 61772508, and Grant 61801428, in part by the Shenzhen Technology Project under Grant JCYJ20180507182610734, Grant JCYJ20170413152535587, and Grant JSGG20170413171746130, and in part by the CAS Key Technology Talent Program, Zhejiang Provincial Natural Science Foundation of China under Grant LY18F020034. Funding Information: 1Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China 2Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong 3Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan 4School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China Publisher Copyright: {\textcopyright} 2013 IEEE.",
year = "2019",
doi = "10.1109/ACCESS.2019.2960520",
language = "English",
volume = "7",
pages = "185179--185188",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}