Wednesday, 12 July 2017

WHAT IS EMBEDDED VISION

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In recent years, a miniaturization trend has been established in many areas of electronics. For example, ICs have become more and more integrated and circuit boards in the electrical industry have become smaller and more powerful. This has also made PCs, mobile phones and cameras more and more compact and powerful. This trend can also be observed in the world of vision technology.
A classic machine vision system consists of an industrial camera and a PC: Both were significantly larger a few years ago. But within a short time, smaller and smaller-sized PCs became possible, and in the meantime, the industry saw the introduction of single-board computers, i.e. computers that can be found on a single board. At the same time, the camera electronics became more compact and the cameras successively smaller. On the way to even higher integration, small cameras without housings are now offered, which can be easily integrated into compact systems.

Due to these two developments, the reduction in size of the PC and the camera, it is now possible to design highly compact camera vision systems for new applications. Such systems are called embedded (vision) systems.

Design and use of an embedded vision system

An embedded vision system consists, for example, of a camera, a so-called board level camera, which is connected to a processing board. Processing boards take over the tasks of the PC from the classic machine vision setup. As processing boards are much cheaper than classic industrial PCs, vision systems can become smaller and also more cost-effective. The interfaces for embedded vision systems are primarily USB or Basler BCON for LVDS.

Basler Camera Distributor in India

Embedded vision systems are used in a wide range of applications and devices, such as in medical technology, in vehicles, in industry and in consumer electronics. Embedded systems enable new products to be created and thereby create innovative possibilities in several areas.

Which embedded systems are available?

As embedded systems, there are popular single-board computers (SBC), such as the Raspberry Pi®. The Raspberry Pi ® is a mini-computer with established interfaces and offers a similar range of features as a classic PC or laptop.

Embedded vision solutions can also be implemented with so-called system on modules (SoM) or computer on modules (CoM). These modules represent a computing unit. For the adaptation of the desired interfaces to the respective application, a so-called individual carrier board is needed. This is connected to the SoM via specific connectors and can be designed and manufactured relatively simply. The SoMs or CoMs (or the entire system) are cost-effective on the one hand since they are available off-the-shelf, while on the other hand they can also be individually customized through the carrier board.

For large manufactured quantities, individual processing boards are a good idea.

All modules, single-board computers, and SoMs, are based on a system on chip (SoC). This is a component on which the processor(s), controllers, memory modules, power management and other components are integrated on a single chip.

Due to these efficient components, the SoCs, embedded vision systems have only become available in such a small size and at a low cost as today.

Characteristics of embedded vision systems versus standard vision systems

Most of the above-mentioned single-board computers and SoMs do not include the x86 family processors common in standard PCs. Rather, the CPUs are often based on the ARM architecture. 

The open-source Linux operating system is widely used as an operating system in the world of ARM processors. For Linux, there is a large number of open-source application programs, as well as numerous freely-available program libraries.

Increasingly, however, x86-based single-board computers are also spreading.

A consistently important criterion for the computer is the space available for the embedded system.
For the SW developer, the program development for an embedded system is different than for a standard PC. As a rule, the target system does not provide a suitable user interface which can also be used for programming. The SW developer must connect to the embedded system via an appropriate interface if available (e.g. network interface) or develop the SW on the standard PC and then transfer it to the target system. 

When developing the SW, it should be noted that the HW concept of the embedded system is oriented to a specific application and thus differs significantly from the universally usable PC.

However, the boundary between embedded and desktop computer systems is sometimes difficult to define. Just think of the mobile phone, which on the one hand has many features of an embedded system (ARM-based, single-board construction), but on the other hand can cope with very different tasks and is therefore a universal computer.

What are the benefits of embedded vision systems?

In some cases, much depends on how the embedded vision system is designed. A single-board computer is often a good choice as this is a standard product. It is a small compact computer that is easy to use. This solution is also useful for developers who have had little to do with embedded vision. 

On the other hand, however, the single-board computer is a system which contains unused components and thus generally does not allow the leanest system configuration. This solution is suitable for small to medium quantities. The leanest setup is obtained through a customized system. Here, however, higher integration effort is a factor. This solution is therefore suitable for large unit numbers.

The benefits of embedded vision systems at a glance:
  • Lean system design
  • Light weight
  • Cost-effective, because there is no unnecessary hardware
  • Lower manufacturing costs
  • Lower energy consumption
  • Small footprint

Which interfaces are suitable for an embedded vision application?

Embedded vision is the technology of choice for many applications. Accordingly, the design requirements are widely diversified. Depending on the specification, Basler offers a variety of cameras with different sensors, resolutions and interfaces.

The two interface technologies that Basler offers for embedded vision systems in the portfolio are:
  • USB3 Vision for easy integration and
  • Basler BCON for LVDS for a lean system design
Both technologies work with the same Basler pylon SDK, making it easier to switch from one interface technology to the other.

USB3 Vision

USB 3.0 is the right interface for a simple plug and play camera connection and ideal for camera connections to single-board computers. The Basler pylon SDK gives you easy access to the camera within seconds (for example, images and settings), since USB 3.0 cameras are standard-compliant and GenICam compatible.

Benefits
  • Easy connection to single-board computers with USB 2.0 or USB 3.0 connection
  • Field-tested solutions with Raspberry Pi®, NVIDIA Jetson TK1 and many other systems
  • Profitable solutions for SoMs with associated base boards
  • Stable data transfer with a bandwidth of up to 350 MB/s

BCON for LVDS

BCON - Basler's proprietary LVDS-based interface allows a direct camera connection with processing boards and thus also to on-board logic modules such as FPGAs (field programmable gate arrays) or comparable components. This allows a lean system design to be achieved and you can benefit from a direct board-to-board connection and data transfer. 

The interface is therefore ideal for connecting to a SoM on a carrier / adapter board or with an individually-developed processor unit.

If your system is FPGA-based, you can fully use its advantages with the BCON interface.
BCON is designed with a 28-pin ZIF connector for flat flex cables. It contains the 5V power supply together with the LVDS lanes for image data transfer and image triggering. You can configure the camera vialanes that work with the I²C standard.

Basler's pylon SDK is tailored to work with the BCON for LVDS interface. Therefore, it is easy to change settings such as exposure control, gain, and image properties using your software code and pylons API. The image acquisition of the application must be implemented individually as it depends on the hardware used.

Benefits
  • Image processing directly on the camera. This results in the highest image quality, without compromising the very limited resources of the downstream processing board.
  • Direct connection via LVDS-based image data exchange to FPGA
  • With the pylon SDK the camera configuration is possible via standard I²C bus without further programming. The compatibility with the GenICam standard is given.
  • The image data software protocol is openly and comprehensively documented
  • Development kit with reference implementation available
  • Flexible flat flex cable and small connector for applications with maximum space limitations
  • Stable, reliable data transfer with a bandwidth of up to 252 MB/s

How can an embedded vision system be developed and how can the camera be integrated?

Although it is unusual for developers who have not had much to do with embedded vision to develop an embedded vision system, there are many possibilities for this. In particular, the switch from standard machine vision system to embedded vision system can be made easy. In addition to its embedded product portfolio, Basler offers many tools that simplify integration.

Find out how you can develop an embedded vision system and how easy it is to integrate a camera in our simpleshow video.

Machine learning in embedded vision applications

Embedded vision systems often have the task of classifying images captured by the camera: On a conveyor belt, for example, in round and square biscuits. In the past, software developers have spent a lot of time and energy developing intelligent algorithms that are designed to classify a biscuit based on its characteristics (features) in type A (round) or B (square). In this example, this may sound relatively simple, but the more complex the features of an object, the more difficult it becomes.

Algorithms of machine learning (e.g., Convolutional Neural Networks, CNNs), however, do not require any features as input. If the algorithm is presented with large numbers of images of round and square biscuits, together with the information which image represents which variety, the algorithm automatically learns how to distinguish the two types of biscuits. If the algorithm is shown a new, unknown image, it decides for one of the two varieties because of its "experience" of the images already seen. The algorithms are particularly fast on graphics processor units (GPUs) and FPGAs.




To Know More About Basler Camera Distributor in India, Contact Menzel Vision and Robotics Pvt Ltd at (+ 91) 22 67993158 or Email us at info@mvrpl.com


 

Contact Details



Address: 4, A-Wing, Bezzola Complex,
Sion Trombay Road, Chembur

400071 Mumbai, India
Tel:(+91) 22 67993158
Fax: (+91) 22 67993159
Mobile:+91 9323786005 / 9820143131
E-mail: info@mvrpl.com


 

Tuesday, 20 June 2017

New maintenance release 3.0.1 for MERLIC 3

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The latest MERLIC maintenance release is now available for download on our website and provides minor bug fixes.

In particular, a bug has been fixed within the ADLINK NEON Digital I/O interface, that occurred in some cases when the tool 'Acquire Image from Camera' was used in the same MVApp as the interface.
MVTec Merlic


Furthermore, reading data codes with MERLIC has become more robust with this release.

We recommend all MERLIC 3 users to update their MERLIC installation with this release.

Please keep up-to-date with more features at WWW.MERLIC.COM.





TO KNOW MORE ABOUT MVTEC MACHINE VISION SOFTWARE DEALER MUMBAI, INDIA, CONTACT MENZEL VISION AND ROBOTICS PVT LTD AT (+ 91) 22 67993158 OR EMAIL US AT INFO@MVRPL.COM



Contact Details



Address: 4, A-Wing, Bezzola Complex,
Sion Trombay Road, Chembur

400071 Mumbai, India
Tel:(+91) 22 67993158
Fax: (+91) 22 67993159
Mobile:+91 9323786005 / 9820143131
E-mail: info@mvrpl.com

Thursday, 23 March 2017

IMPERX Welcomes Keith Wetzel as Director of New Product Development

IMPERX, Inc., a designer and manufacturer of industrial cameras and frame grabbers, has named industry veteran Keith Wetzel as Director of New Product Development. Wetzel will focus on strategic marketing initiatives and new product development aimed at driving continued customer satisfaction with winning imaging solutions.

"Keith brings over 28 years of results-oriented expertise in image sensor technology from TRUESENSE Imaging, Inc./Eastman Kodak Company," says Petko Dinev, President and CEO of IMPERX. "He will be a great addition to our senior management team and tremendous asset to our company's growth plan."

Wetzel joins IMPERX from TRUESENSE Imaging, Inc., formerly Eastman Kodak Company Image Sensor Solutions group, where he held various product and sales management roles during his 19-year tenure within the image sensor business and 28 year employment at Eastman Kodak Company. In his most recent assignment, he held the position of business development within the Americas region.

Wetzel received his bachelor's degree in electrical engineering and master's degree in business administration from Rochester Institute of Technology. He also holds several patents and contributed several image sensor articles to various photonics publications over his career.

About IMPERX  IMPERX designs and manufactures high performance cameras, frame grabbers and industrial imaging systems used in the machine vision, medical, military, surveillance and automation markets. Our multi-service brand is recognized for superior performance, reliability, accuracy, and cutting-edge design. Headquartered in Boca Raton, FL, with offices and distributors worldwide, IMPERX is registered to the ISO 9001:2008 Quality Management System Standard and the Environmental Management System Standard ISO 14001:2004. IMPERX is registered with DDTC.

Tuesday, 7 March 2017

Tailor-Made for You and Your Swing

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How CCR determines the best golf club using slow motion analysis

At first glimpse, swinging a golf club looks really easy. However, it requires a high level of precision, physical control and coordination and is among the most technically challenging motion sequences in sport. The ball lies just above the ground and has to be hit accurately with a club head measuring just ten by five centimetres. On impact, the golfer must hit the face of the club at a precise right angle and while doing so, take the ground conditions, distances, wind strength and gradients into account.

Reasons for Custom-Made Golf Clubs

Selecting the right club is crucial to the perfect golf swing. The club must be tailored to the individual measurements of the player as well as his or her unique requirements. If, for example, the club is too long, it becomes difficult to control. On the other hand, if it is too short, the player has to stoop, and achieving the correct shoulder turn from such a stance is challenging.

In golf businesses or specialist departments, clubs tend to come in standard versions. These naturally suit players who match such standard measurements. However, the majority of players don't fit the norm and require clubs that are custom-made. 

This is where Mr. Erhardt brings his fitting service into play. The first step is to measure his customers. The ideal club length can be determined by using body height, distance from hand to floor and other factors. Then an analysis is performed of body movements. To do so, Mr. Erhardt uses the MotionBLITZ EoSens® mini1 high-speed camera.

Slow-Motion Analysis

On average, a golf stroke is executed in just 0.8 - 1.5 seconds. The club head can reach a speed of up to 200 km/h when wielded by an experienced player. But such details of a swing can't be picked up with the naked eye. The MotionBLITZ EoSens® mini1 provides detailed slow-motion recordings, which Dietmar Erhardt analyses.

The recordings provide important findings relating to the best material to choose for the shaft. For example, the greater the club head speed in relation to timing, the more the shaft is inclined to bend out of shape. For players with a fast, powerful swing, a less flexible shaft is therefore recommended. This allows the player to have increased control over the stroke. For older players or beginners with a slower, weaker swing, a shaft made from a more supple material is advised. With a shaft made from graphite, the weight shifts to the club head and the total weight is reduced, enabling improved acceleration.

The cameras feature compact, robust housing (29 X 29×43 MM) which allows for easy installation - even in tight spaces. The compact form factor satisfies the requirements of transportation departments for discrete installation while providing efficient monitoring. The cameras are also shock-resistant so that camera-shake and blurred images can be avoided; GigE interfaces enable cable lengths of up to 100 meters.




To Know More About Mikrotron High Speed Camera Distributor in India, Contact Menzel Vision and Robotics Pvt Ltd at (+ 91) 22 67993158 or Email us at info@mvrpl.com


Contact Details



Address: 4, A-Wing, Bezzola Complex,
Sion Trombay Road, Chembur

400071 Mumbai, India
Tel:(+91) 22 67993158
Fax: (+91) 22 67993159
Mobile:+91 9323786005 / 9820143131
E-mail: info@mvrpl.com

Source - mikrotron.de

Tuesday, 14 February 2017

Vision System Inspects X-ray Dosimeter Badges – Helmholtz-Zentrum

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In Germany, the inspection of x-ray dosimeters worn by people who may be exposed to radiation is a governmental responsibility. Only a handful of institutions are qualified to perform such tasks. One of which, the Helmholtz-Zentrum (Munich, Germany) is responsible for the analysis of approximately 120,000 film badge dosimeters a month.


Previously these 120,000 film badges were evaluated manually. To speed this inspection and increase reliability, the Helmholtz-Zentrum has developed a machine-vision system to automatically inspect these films. The film from each dosimeter badge is first mounted on a plastic adhesive foil, which is wound into a coil. This coil is then mounted on the vision system so that each film element can be inspected automatically (see figure). To analyze each film, a DX4 285 FireWire camera from Kappa optronics (Gleichen, Germany) is mounted on a bellows stage above the film reel. 

Data from this camera is then transferred to a PC and processed using HALCON 9.0 from MVTec Software (Munich, Germany). Resulting high-dynamic-range images are then displayed using an ATIFire GL V3600 graphic board from AMD (Sunnyvale, CA, USA) on a FlexScan MX 190 S display from Eizo (Ishikawa, Japan). Before the optical density of the film is measured, its presence and orientation must be determined. As each film moves under the camera system’s field of view, this presence and orientation task is computed using HALCON’s shape-based matching algorithm.

Both the camera and a densitometer are used to measure the optical density of the film. The densitometer measures the brightness at each of seven points on the film in high precision and is used to calibrate the camera measurement for every film image. To increase the dynamic range of the gray-level image of the film, two images with different exposure times are computed and combined into a high-dynamic-range image. Because the background lighting is not homogenous, shading correction is performed to eliminate any lighting variation. Any lens vignetting and variations caused by pixel-to-pixel sensitivity variation is eliminated by flat-field correction. The optical density is converted into a photon dose using a linear algebraic function to calculate the x-ray dose to which the film was exposed.

Every film reading must be correlated with the unique specimen number associated with each badge. Since these numbers are deposited onto the film material, approximately 10,000 characters needed to be trained and saved to an OCR database using HALCON. After the film is identified, the system must also detect which type of dosimeter cassette has been used to house the film. Since each cassette uses a different x-ray filter, the shadow cast on the film can be either rectangular or round. Thus, a grayscale analysis of these shadows can be used to detect the differences between the different types of cassettes that were used to house the film. To pinpoint the specific causes of x-ray exposure, the system is also programmed to detect whether any potential exposure is caused by errors in film developing or x-ray contamination. If the imaging system detects contamination events, these are then reported manually.






To Know More About Machine Vision System in India, Contact Menzel Vision and Robotics Pvt Ltd at (+ 91) 22 67993158 or Email us at info@mvrpl.com

 

 

Contact Details



Address: 4, A-Wing, Bezzola Complex,
Sion Trombay Road, Chembur

400071 Mumbai, India
Tel:(+91) 22 67993158
Fax: (+91) 22 67993159
Mobile:+91 9323786005 / 9820143131
E-mail: info@mvrpl.com

 



Source - mvtec.com

Tuesday, 7 February 2017

Industrial Cameras - Letting Robotic Arms See

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Robotic arms are widely used in industrial automation. They complete tasks which humans cannot accomplish, are considered too time consuming or dangerous, or which require precise positioning and highly repetitive movements. Tasks are completed in high quality with speed, reliability and precision. Robotic arms are used in all areas of industrial manufacturing from the automobile industry to mold manufacturing and electronics but also in fields where the technology might be less expected such as agriculture, healthcare and service industries.

Robotic Arms "See" with Machine Vision

Like humans, robotic arms need "eyes" to see and feel what they grasp and manipulate: machine vision makes this possible. Industrial cameras and image processing software work together to enable the robot to move efficiently and precisely in three dimensional space which enables them to perform a variety of complex tasks: welding, painting, assembly, picking and placing for printed circuit boards, packaging and labeling, palletizing, product inspection, and high-precision testing. Not all industrial cameras are compatible with or can be installed in robotic arms, but The Imaging Source's GigE industrial cameras provide an optimal solution.

GigE Industrial Cameras from The Imaging Source - The Cost Effective and Highly Versatile Imaging Solution

The Imaging Source's GigE industrial cameras are best known for their outstanding image quality, easy integration and rich set of features. They are shipped with highly sensitive CCD or CMOS sensors from Sony and Aptina, which offer very low noise levels, provide multiple options in terms of resolution and frame rate, guarantee precise positioning capture and output first-rate image quality. External Hirose ports make the digital I/O, strobe, trigger inputs and flash outputs easily accessible. Binning and ROI features (CMOS only) enable increased frame rates and improved signal to noise ratios. The cameras' extremely compact and robust industrial housing means straightforward integration into robotic assemblies.

In addition, The Imaging Source's GigE industrial cameras are shock-resistant, so camera-shake and blurred images can be avoided. The cameras are shipped with camera-end locking screws, and the built-in Gigabit Ethernet interface allows for very long cable lengths (up to100 meters) for maximum flexibility.

The Imaging Source's GigE industrial cameras come bundled with highly compatible end-user software and SDKs which makes the setup and integration with robotic arms fast and simple. Trained personnel without extensive robot programming experience can reprogram the cameras to complete new tasks in a snap. These camera characteristics, along with their competitive price, make The Imaging Source GigE industrial cameras the perfect solution for robotic arm applications.


Suitable cameras for robotic arms:
  • GigE color industrial cameras
  • GigE monochrome industrial cameras




To Know More About Imaging Source Machine Vision Cameras in India, Contact Menzel Vision and Robotics Pvt Ltd at (+ 91) 22 67993158 or Email us at info@mvrpl.com


Contact Details



Address: 4, A-Wing, Bezzola Complex,
Sion Trombay Road, Chembur

400071 Mumbai, India
Tel:(+91) 22 67993158
Fax: (+91) 22 67993159
Mobile:+91 9323786005 / 9820143131
E-mail: info@mvrpl.com

Tuesday, 31 January 2017

Vision Helps Spot Failures on the Rail - Network Rail Ltd.

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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 in India, Contact Menzel Vision and Robotics Pvt Ltd at (+ 91) 22 67993158 or Email us at info@mvrpl.com


Contact Details



Address: 4, A-Wing, Bezzola Complex,
Sion Trombay Road, Chembur

400071 Mumbai, India
Tel:(+91) 22 67993158
Fax: (+91) 22 67993159
Mobile:+91 9323786005 / 9820143131
E-mail: info@mvrpl.com

Source - mvtec.com