Reference
S. Faghih-Roohi, S. Hajizadeh, A. Núñez, R. Babuska, and B. De
Schutter, "Deep convolutional neural networks for detection of rail surface
defects,"
Proceedings of the 2016 International Joint
Conference on Neural Networks (IJCNN 2016), Vancouver, Canada, pp.
2584-2589, July 2016.
Abstract
In this paper, we propose a deep convolutional neural network solution to the
analysis of image data for the detection of rail surface defects. The images
are obtained from many hours of automated video recordings. This huge amount of
data makes it impossible to manually inspect the images and detect rail surface
defects. Therefore, automated detection of rail defects can help to save time
and costs, and to ensure rail transportation safety. However, one major
challenge is that the extraction of suitable features for detection of rail
surface defects is a non-trivial and difficult task. Therefore, we propose to
use convolutional neural networks as a viable technique for feature learning.
Deep convolutional neural networks have recently been applied to a number of
similar domains with success. We compare the results of different network
architectures characterized by different sizes and activation functions. In
this way, we explore the efficiency of the proposed deep convolutional neural
network for detection and classification. The experimental results are
promising and demonstrate the capability of the proposed approach.
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BibTeX
@inproceedings{FagHaj:16-002,
author = {Faghih-Roohi, Shahrzad and Hajizadeh, Siamak and
N{\'{u}}{\~{n}}ez, Alfredo and Babuska, Robert and De Schutter,
Bart},
title = {Deep Convolutional Neural Networks for Detection of Rail
Surface Defects},
booktitle = {Proceedings of the 2016 International Joint Conference on
Neural Networks (IJCNN 2016)},
address = {Vancouver, Canada},
pages = {2584--2589},
month = jul,
year = {2016}
}