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.