Potential Dangerous Object Detection On
Railway Ballast Using Digital Image Processing
Author(s)
P. L. Mazzeo, E. Stella, M. Nitti & A. Distante
Abstract
The correct assessment of the condition of a railroad requires the consideration
of different factors. Some factors, such as the condition of the ties, can be
measured by inspecting features visible from the surface of the railway. Other
factors include the condition of the ballast; it is important to recognize the
critical situation in which any foreign object can be present on the ballast. These
kinds of objects could be cans, pieces of sheet and everything over a well
determined dimension. Extensive human resources are currently applied to the
problem of evaluating railroad health. The proposed visual inspection system
uses images acquired from a digital line scan camera installed under a train.
Here we focus on the problem of foreign object detection in the railway
maintenance context. To obtain this aim we train a Multilayer Perceptron
Network (MLPN) with the edge histogram of the ballast patches manually
extracted from the acquired digital image sequence. The general performances of
the system, in terms of speed and detection rate, are mainly influenced by the
adopted features for representing images and by their number. By this inspection
system it is possible to aid the personnel in railway safety issues because a high
detection rate percentage has been obtained. We show the adopted techniques by
using images acquired in real experimental conditions.
Keywords: obstacle detection, ballast inspection, neural networks.
1 Introduction
Inspection of the rail state is one of the basic tasks in railway maintenance. In the
last few years a large number of methods have been proposed by the computer
vision community for facing the problem of visual inspection [1, 2]. The
Keywords
obstacle detection, ballast inspection, neural networks.
Related Book
Other papers in this volume
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