Material classification and semantic segmentation of railway track images with deep convolutional neural networks

@article{Giben2015MaterialCA,
  title={Material classification and semantic segmentation of railway track images with deep convolutional neural networks},
  author={Xavier Giben and Vishal M. Patel and Rama Chellappa},
  journal={2015 IEEE International Conference on Image Processing (ICIP)},
  year={2015},
  pages={621-625}
}
The condition of railway tracks needs to be periodically monitored to ensure passenger safety. Cameras mounted on a moving vehicle such as a hi-rail vehicle or a geometry inspection car can generate large volumes of high resolution images. Extracting accurate information from those images has been challenging due to background clutter in railroad environments. In this paper, we describe a novel approach to visual track inspection using material classification and semantic segmentation with Deep… CONTINUE READING

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