Multi-scale dilated convolution network based depth estimation in intelligent transportation systems

Yanling Tian, Qieshi Zhang*, Ziliang Ren, Fuxiang Wu, Pengyi Hao, Jinglu Hu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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.

Original languageEnglish
Article number8936425
Pages (from-to)185179-185188
Number of pages10
JournalIEEE Access
Publication statusPublished - 2019


  • Depth estimation
  • ResNet
  • dilated network
  • intelligent transportation systems (ITS)
  • multi-scale dilated module

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


Dive into the research topics of 'Multi-scale dilated convolution network based depth estimation in intelligent transportation systems'. Together they form a unique fingerprint.

Cite this