An automated vision inspection system relieves rail workers from the task of manually inspecting rail infrastructure
Traditionally, rail infrastructure has been
inspected manually by foot patrols walking the entire length of a rail
network to visually determine whether any flaws exist that could result
in failures. Needless to say, the method is extremely labor intensive
and time consuming.
To minimize the disruption to train services, the
manual inspection process is usually performed overnight and at
weekends. However, due to the increase in passenger and freight traffic
on rail networks, the time that can be allocated to access the rail
infrastructure by foot patrols is now at a premium. Hence rail
infrastructure owners are under pressure to find more effective means to
perform the task.
To reduce the time required to inspect its rail
network, UK infrastructure owner Network Rail (London, England;
www.networkrail.co.uk) is now deploying a new vision-based inspection
system that looks set to replace the earlier manual inspection process.
Not only will the system help to increase the availability -- and assure
the safety -- of its rail network, it will also enable the organization
to determine the condition of the network with greater consistency and
accuracy.
Developed by Omnicom Engineering (York, UK;
www.omnicomengineering.co.uk), the OmniVision system has been designed
to automatically detect the same types of flaws that would be spotted by
foot patrols. These include missing fasteners that hold the rail in
place on sleepers and faults in weak points in the infrastructure such
as at rail joints where lengths of rail are bolted together. The system
will also detect the scarring of rail heads, incorrectly fitted rail
clamps and any issues with welds that join sections of rail together to
form one continuous rail.
System Architecture
The OmniVision system comprises an array of seven
2048 x 1 pixel line scan cameras, four 3D profile cameras, a sweeping
laser scanner and two thermal cameras. Fitted to the underside of a rail
inspection car, the vision system illuminates the rail with an array of
LED line lights and acquires images of the track and its surroundings
as the car moves down the track at speeds of up to 125mph (Figure 1).
The on-board vision system is complemented by an off-train processing
system located at the Network Rail in Derby that processes the data to
determine the integrity of the rail network.
For every 0.8mm that the inspection vehicle
travels, a pair of three line scan cameras housed in rugged enclosures
capture images of each of the rail tracks. Two vertically positioned
cameras image the top surface or the head of each of the rails, while
the other four are positioned at an angle to capture images of the web
of the rail. A seventh centrally-located line scan camera captures
images of the area between the two rails from which the condition of the
ballast and the rail sleepers and the location and condition of other
rail assets that complement the signaling system can be determined.
The cameras transfer image data to frame grabbers
in a PC-based 19in rack system on board the train over a Camera Link
interface. The frame grabbers were designed in-house to ensure that the
data transfer rate from the cameras could be maintained at a rate of
approximately 145MBytes/s and that no artifacts within the images are
lost through compression. Once captured, the images from each of the
cameras are then written to a set of 1TByte solid state drives.
Within the same rugged enclosure as the line scan
cameras, the pair of thermal cameras mounted at 45° angles point to the
inside web of each of the rails. Their purpose is to capture thermal
data at points such as rail joints which can expand and contract
depending on ambient temperature. Both the thermal cameras are
interfaced via GigE connections to a frame grabber in the on-board 19in
rack and the images from them are also stored on 1TByte solid state
drives.
Further down the inspection vehicle, two pairs of
3D profile cameras capture a profile of the rails and the area
surrounding them for every 200mm that the vehicle travels. Data from the
four cameras are transferred to the 19in rack-mounted system over a
GigE interface to a dedicated frame grabber and the data again stored on
TByte drives. Data acquired by the cameras is used to build a 3D image
of the rails and the fasteners used to hold the rails to the sleepers
and the ballast around them.
In addition to the line scan, thermal and 3D
profile cameras, the system also employs a centrally-mounted sweeping
laser scanner situated on the underside of the inspection vehicle which
covers a distance of 5m on either side of the rails. Data from the laser
scanner - which is transferred to the 19in rack-mounted system over an
Ethernet interface and also stored on a set of Terabyte drives - is used
to determine whether or not the height of the surrounding ballast on
the rail is either too high or deficient.
Processing Data
In operation, a vehicle fitted with the imaging
system acquires around 5TBytes of image data in a single shift over a
distance of around 250 miles. Once acquired, the image data from all the
cameras is indexed with timing and GPS positional data such that the
data can be correlated prior to processing. Data acquired from the
cameras during a shift is then transmitted to the dedicated processing
environment at Network Rail, where it is transferred onto a 500TByte
parallel file storage system at an aggregate data rate of around 2GB/s
for a single data set.
Because the image data is tagged with the
location and time at which it was acquired, it is possible to establish
the start and end of a specific patrol, or part of a single shift. The
indexed imagery associated with each patrol is then subdivided into
sections representing several hundred meters of rail infrastructure,
after which it is farmed out to a dedicated cluster set of Windows-based
servers, known as the image processing factory. Once one set of image
data relating to one section of rail has been analyzed by the processing
cluster of 20 multi-core PC-based servers and the results returned, a
following set of data is transferred into the processors until an entire
patrol has been analyzed.
To process the images acquired by the cameras,
the OmniVision system uses the image processing functions in MVTec's
(Munich, Germany; www.mvtec.com) HALCON software library. Typically, the
images acquired by the line scan cameras are first segmented to
determine regions of interest - such as the location of the rail. Once
the location of the rail has been found, it is possible to establish an
area of interest around the rail where items such as fasteners, clamps
and rail joints should be located. A combination of edge detection and
shape-based matching algorithms are then used to determine whether a
fastener, clamp or rail joint has been identified by comparing the image
of the objects with models stored on the database of the system (Figure
2).
To verify that objects such as fasteners or clamp
are present, missing, or being obscured by ballast, a more detailed
analysis is performed on the data acquired by the 3D profile cameras as a
secondary check. To do so, the 3D profile data is analyzed using
HALCON's 3D pattern matching algorithm to determine the 3D position and
orientation of the objects even if they are partially occluded by
ballast (Figure 3). Should the software be unable to match the 3D data
with a 3D model of the object, the potential defect - known as a
candidate - is flagged for further analysis and returned to a database
for manual verification.
The system can also determine the condition of
welds in the rail. As the vision inspection system moves over each of
the welds, the line scan cameras capture an image of each one. From the
images, the software can perform shape-based matching to identify
locations where a potential joint failure may exist. Any potential
failure of the weld is also flagged as a potential candidate for further
investigation. Similarly, the 3D-based model created from data captured
by the laser scanner can also be analyzed by the software to determine
if the height of the ballast in and around the track is within
acceptable limits.
Identifying defects
Through OmniVision's Viewer application - which
runs on a set of eight PCs connected to the server - track inspectors
are visually presented with a breakdown of the defects along with the
images associated with them. This allows them to navigate through,
review and prioritize any defects that the system may have detected.
Once a defect has been identified, the operators can then schedule the
necessary repairs to be carried out manually by on-track teams.
To date, three Omnicom vision systems have been
fitted to Network Rail inspection vehicles and effectively used to
determine the condition of the UK's West Coast mainline network.
Currently, two additional systems are being commissioned and by the end
of this year, Network Rail plans to roll the system out to cover the
East Coast main line between London and Edinburgh and the Great Western
mainline from London to Wales. When fully operational, the fleet of
inspection vehicles will inspect more than 15,000 miles of Network
Rail's rail network per fortnight, all year round.
To Know More About Machine Vision System Singapore, Contact MVAsia Infomatrix Pte Ltd at +65 6329-6431 or Email us at info@mvasiaonline.com
MV ASIA INFOMATRIX PTE LTD
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Orchard Road
Singapore - 048617
Tel: +65 63296431
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Source - xenics.com
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