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Road recognition based on multi-scale convolutional network with multi-level feature fusion
Author(s): Ye Li; Lili Guo; Lele Xu; Xianfeng Wang; Shan Jin
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Paper Abstract

Road recognition from optical remote sensing images is important for many applications like intelligent transportation system. Currently, convolutional neural networks (CNNs) methods are widely utilized in road recognition. However, many CNN methods hardly get good recognition performance when they process high-resolution images with large road width variance and complex background. For this problem, we develop a road recognition method based on a multi-scale convolutional network (MCN) with multi-level feature fusion. MCN comprises several CNNs with different scales of inputs and thus can extract multi-scale features. Each CNN fuses low-level geometrical features, middle-level features and high-level semantic features respectively from shallow, middle and deep layers. The multi-scale scheme and multi-level feature fusion make the MCN capable to handle large road width variance and complex background. Our method is validated on a manually labeled visible remote sensing image dataset. Moreover, our method is compared with CNNs without multi-scale or multi-level feature fusion and a fully convolutional network (FCN). The experimental results show that our method can well deal with complex visible remote sensing images with large road width variance.

Paper Details

Date Published: 6 May 2019
PDF: 7 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110693F (6 May 2019); doi: 10.1117/12.2524175
Show Author Affiliations
Ye Li, Technology and Engineering Ctr. for Space Utilization (China)
Lili Guo, Technology and Engineering Ctr. for Space Utilization (China)
Tsinghua Univ. (China)
Lele Xu, Technology and Engineering Ctr. for Space Utilization (China)
Xianfeng Wang, Technology and Engineering Ctr. for Space Utilization (China)
Shan Jin, Technology and Engineering Ctr. for Space Utilization (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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