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Integration of machine vision into industrial automation systems
 Camera image processing and visual inspection is an increasingly common part of today's industrial automation systems. Therefore, the requirement for cooperation of vision systems with control and automation systems is also of great importance. The VisionLab machine vision system can be directly installed into the Control Web software environment. Integrating visual inspection tasks into industrial automation systems is therefore as efficient as possible.

This interconnection within one program environment significantly simplifies not only the development of new applications, but also the resulting solution is cheaper than other concepts. In most cases, we can use a single computer to which both cameras and industrial input / output units are connected and which is also connected to a computer network and company information system. This computer usually runs the graphical user interface of the application, the database SQL server, the web server, visual inspection and machine vision tasks, as well as the entire logic of the automation application.

Fig. 1: Machine vision as part of the control system. The range of functionality makes Control Web an effective industry digitization tool for virtually any customized solution.

Fig. 1: Machine vision as part of the control system. The range of functionality makes Control Web an effective industry digitization tool for virtually any customized solution.

If additional remote automation racks are needed, each can be connected via Ethernet and TCP/IP networks. An example of such a remote switchboard is shown in Fig. 2. The efficiency of the solution and the advantageous price of all hardware and software is obvious in such conceived systems.

Fig. 2: The automation switchboard is connected with a single Ethernet cable thanks to DataLab units with TCP/IP protocol.

Fig. 2: The automation switchboard is connected with a single Ethernet cable thanks to DataLab units with TCP/IP protocol.

With the advent of the new generation of Control Web 8, several features have been added to further enhance machine vision integration capabilities.

Fig. 3: Demonstration of stereoscopic capturing of outdoor scene space. The virtual instrument builds a 3D model of the sensed space and it can determine eg. the position of cars and their distance from the camera.

Fig. 3: Demonstration of stereoscopic capturing of outdoor scene space. The virtual instrument builds a 3D model of the sensed space and it can determine eg. the position of cars and their distance from the camera.

A new type of data data objects for transferring general binary data has been added to machine vision. This type corresponds to data elements of the data type in the Control Web environment. Virtual instruments in the application program and steps in the machine vision chain can now efficiently exchange blocks of arbitrary data. The type of data and the format of its storage is not prescribed in any way. In some tasks, such as further work with detected significant image points in an application program, this technique of transferring binary data blocks brings significant acceleration. New possibilities of cooperation of automation application with a chain of machine vision steps can be well demonstrated eg by virtual instruments for displaying spatial scene in the form of 3D object. The image data is captured by a stereoscopic pair of cameras. This virtual instrument has several peculiarities. It depicts a scene captured by cameras, but does not communicate directly with the cameras itself. It needs not only two images from cameras, but also information about feature points and their descriptors found in these two images. It must therefore cooperate with machine vision, which, by means of the point detection step, fills data elements of the data type with found descriptors of these points. Similar to the point descriptors, the actual image from each camera is transmitted to the virtual instrument via data elements of the data type. A virtual instrument can be very useful in applications where we need information about the distances of objects in an image, when we have to find objects at different distances or avoid collisions with objects in space while moving. By means of data elements of the data type, any image can also be transmitted, for example, to display devices located anywhere in the 3D space of the scene.

Fig. 4: 3D model of the captured scene, created from two camera images

Fig. 4: 3D model of the captured scene, created from two camera images

We can also use VisionLab's ability to locate feature image points and calculate descriptors describing their surroundings efficiently and with maximum use of massively parallel GPU performance, to detect general objects in images. Feature points are searched on a scale of several scales, then objects can be detected independently of their rotation and brightness, but also independently of their size. A set of descriptors that characterize objects that we want to identify further in the image data can provide detection steps in both files and data elements of the data type.

Fig. 5: In 3D space model it is possible to measure distances of individual surfaces from cameras

Fig. 5: In 3D space model it is possible to measure distances of individual surfaces from cameras

Fig. 6: Via the detected feature points, eg. traffic signs can be identified in the camera image

Fig. 6: Via the detected feature points, eg. traffic signs can be identified in the camera image

Now that the new eighth generation of Control Web is available, it is also worth noting that the development version of Control Web can be downloaded free of charge from www.controlweb.eu and used on an unlimited number of free installations. The license is only required if the application is required to run continuously. All VisionLab drivers and vision systems can be used as well.

Fig. 7: The object you are looking for is enough to mark and save a set of descriptors that characterizes it ...

Fig. 7: The object you are looking for is enough to mark and save a set of descriptors that characterizes it ...

Fig. 8: ... and then it can be reliably detected in the video stream. All feature points of the current image are highlighted in red.

Fig. 8: ... and then it can be reliably detected in the video stream. All feature points of the current image are highlighted in red.

Roman Cagaš

 
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