RAIL CORRUGATION DETECTION BY GABOR FILTERING
Clelia Mandriota, Ettore Stella, Massimiliano Nitti, Nicola Ancona, Arcangelo Distante
Istituto Elaborazione Segnali ed Immagini – C.N.R.
Via Amendola, 166/5 – 70126 Bari (Italy)
ABSTRACT
Inspection of the rail state in order to detect defects is
one of the basic tasks in railway maintenance. Rail
defects exhibit different properties and are divided in
various categories relating to the type and position of
flaws on the rail.
In this paper, we propose a technique, based on texture
analysis of rail surface, to detect and classify a particular
class of defects: corrugation.
1. INTRODUCTION
Rail defects are due to several kinds of interrelated factors
such as the type of rail, the construction conditions, the
speed and/or frequency of trains using the rail. With
growing of the high-speed traffic on the rail tracks, the
current research is oriented to develop inspection system
able to detect automatically the rail defects without a
human operator. In the past, the presence of human
operator, during periodical visual monitoring in the
detection defect phase, made inspection system slow and
expensive. As alternative special wagons were equipped
with any kind of sensors for monitoring of railway
components. Aim of this paper is to present a technique
able to detect the presence/absence of a defect and
automatically classify it. This rail inspection technique is
based on analysis of textured rail surface. Texture
analysis is important in many applications of
classification, inspection and segmentation of images
based on local spatial pattern of intensity. In the paper we
propose a technique to detect rail corrugation (see Figure
1) that is a particular class of defects that produce an
undulatory deformation on the head of the rail. In high
speed train, the corrugation induces harmful vibrations
on wheel and on its components, reducing the lifetime.
In the literature, many proposed texture segmentation
schemes are based on a filter bank model, followed by
some energy measure. Objective of the energy measure is
to evaluate the energy in the filter output in a local
region. Filtering approaches include Law mask,
ring/wedge filters, Gabor filter bank, Wavelet transform,
quadrature mirror filters, etc.[5]. We use, for our aims, a
set of Gabor filters. The choice is justified because these
approximate the characteristics of certain cells in the
visual cortex of some mammals [4]. Moreover, Gabor
functions are useful because achieve the lower bounds of
the uncertainty inequalities π 4 1 ≥ Δ Δ u x and
π 4 1 ≥ Δ Δ v y , and achieve optimally joint resolution in
space and spatial frequency [1]. After this phase, a
feature vector is extracted for each filtered Gabor image
evaluating mean and variance as local energy functions.
In fact, the analysis of textured surface involves the
identification of texture attributes which can be used for
segmentation. The paper is organized as follows: section
2 describes the Gabor filters; section 3 describes the
texture features and finally in section 4 experimental
results are shown on real data.
2. GABOR FILTERS
In spatial domain 2D complex Gabor filter is given by
( ) 1 - = j :
( ) ( ) ( ) x jF y x g y x h ′ ⋅ ′ ′ = π 2 exp , , (1)
( )
+ - ⋅ =
2
2
2
2
2
1
exp
2
1
,
y x
y x
y x
y x g
σ σ
σ πσ
where ( ) ( ) θ θ θ θ cos sin , sin cos , y x y x y x + - + = ′ ′ are the
rotated coordinates,
x
σ and
y
σ are the standard
deviations of Gaussian envelope along the x and y
directions, F and θ are frequency and orientation
respectively [7]. We consider only circularly symmetric
Gaussians then σ σ σ = =
y x
. Thus (1) is a complex
sinusoidal grating modulated by a 2D gaussian function
626 0-7803-6725-1/01/$10.00 ©2001 IEEE