Aerial unstructured road segmentation based on deep convolution neural network

Rui Wang, Feng Pan, Qichao An, Qi DIao, Xiaoxue Feng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Due to the irregular shape, the blurred edge of the road and the occlusion of obstacles on unstructured roads (rural roads, off-road), some networks that achieve good segmentation effect on structured road images have poor effect on unstructured road images. The segmentation of aerial unstructured roads can obtain information on ground objects and understand the development of the area. The use of deep convolutional neural networks to achieve semantic segmentation of roads has always been a hot research direction. In this paper, it is proposed a semantic segmentation network called RD-Net, which achieves road semantic segmentation. The network includes the reflection padding and the stack of 'convolution + pooling' for feature extraction, the dilated residual transition unit to deepen the network and up-sampling for size restore. The proposed network is tested on aerial unstructured road datasets and compared it to other four state of the art deep learning-based road extraction networks. The proposed network performs well on the road segmentation task, and the segmentation accuracy has also improved. This shows that it is effective and available on unstructured road segmentation.

Original languageEnglish
Title of host publicationProceedings of the 38th Chinese Control Conference, CCC 2019
EditorsMinyue Fu, Jian Sun
PublisherIEEE Computer Society
Pages8494-8500
Number of pages7
ISBN (Electronic)9789881563972
DOIs
Publication statusPublished - Jul 2019
Event38th Chinese Control Conference, CCC 2019 - Guangzhou, China
Duration: 27 Jul 201930 Jul 2019

Publication series

NameChinese Control Conference, CCC
Volume2019-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference38th Chinese Control Conference, CCC 2019
Country/TerritoryChina
CityGuangzhou
Period27/07/1930/07/19

Keywords

  • Deep convolutional neural network
  • Dilated residual transition unit
  • Reflection padding
  • Unstructured road segmentation

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Wang, R., Pan, F., An, Q., DIao, Q., & Feng, X. (2019). Aerial unstructured road segmentation based on deep convolution neural network. In M. Fu, & J. Sun (Eds.), Proceedings of the 38th Chinese Control Conference, CCC 2019 (pp. 8494-8500). Article 8865464 (Chinese Control Conference, CCC; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.23919/ChiCC.2019.8865464