IMAGE PROCESSING g
alone, but instead needs a combination – for example, PCB inspection. ‘So, on a PCB there are printed wires,
labelling and a number of soldered components that all need inspecting to make sure they are parallel to the PCB, rather than being tilted in an undesirable way. Inline computational imaging… can mimic what a human is doing when inspecting a challenging surface. A human tilts and varies their viewing angle to inspect a surface, with different perspectives and illumination directions. While traditionally machine vision solutions have not been able to solve this task, with inline computational imaging, machine vision can now mimic this human behaviour.’ In addition to PCBs, the technique
is suited to inspecting challenging surfaces such as metals, or those where there is a combination of printed and metallic surfaces. ‘You can use it on metallic surfaces to
detect cracks,’ Tanner continues. ‘We have also used it to measure connectors, or dark chip sockets with a huge number of tiny metallic pins. Using inline computational imaging enables the socket to be measured to see if there is a label on it, read what’s printed on the label, and also to reconstruct the pins and measure the height of each of these pins.’
Dealing with data Te system does produce a lot of data and therefore requires a fast processor. Tanner explains: ‘As we are over-sampling the scene to capture the different viewing and illumination angles, you have to deal with a much higher data volume than you would have with traditional 2D or 3D imaging cameras, or solutions where only one image is captured before processing takes place. Instead we capture images while an object moves beneath the camera and then we process all that data together.’ AIT’s algorithms are optimised to run on a GPU. While the processing side of inline
computational imaging might be a little slower than traditional imaging methods
The components of an inline computational imaging setup
such as light sectioning and stereo imaging, the technique is much more suited to detecting fine surface details. ‘Te matching between the images is
more robust compared to classical stereo imaging, therefore the reconstruction quality of inline computational imaging is traditionally better,’ Tanner says. ‘It is also better than only doing the light sectioning. Te main challenge for sure is the need to have high computational power.’
Working with commercial cameras Te AIT continues to work on inline computational imaging. ‘In the past we used a multi-line approach where we didn’t use a whole matrix sensor, but only a few lines to speed up the process,’ Tanner says. However, since there are only a few cameras available on the market offering multi-line mode, AIT has made the technique compatible with traditional area scan cameras. ‘We can use each camera available on the market and adapt it to our acquisition frame,’ she adds. AIT has also increased the number of
illumination directions, which makes the reconstruction more robust and gives more detail about the surface of the object. Te technique can be scaled to different
optical resolutions. Tanner explains that, often, the group uses an optical resolution of about 20μm per pixel, because it’s easy to handle. ‘With this we can demonstrate most of what we want to demonstrate,’ she says. ‘We often use coins to illustrate the feasibility of the technology because they have metallic, glossy surfaces and a really fine surface structure. We can reconstruct this very well with inline computational imaging and it demonstrates its comparison with the technical feasibility of other technologies quite well.’ However, for when 20μm isn’t enough,
AIT has developed a microscopic inline computational imaging setup where resolutions of 700nm per pixel can be achieved. ‘Tis is especially interesting for
inspecting ball grid arrays – a type of surface-mount packaging used for integrated circuits – where you can reconstruct the balls of the array,’ Tanner explains. ‘But then we are very limited in depth range because it is like a traditional optical system, where the smaller the resolution is, the smaller the depth range is. But this microscopy solution can also image printed surfaces very well.’ Traditionally, AIT has worked in security
Enhanced, rectified 2D images produced by inline computational imaging 18 IMAGING AND MACHINE VISION EUROPE AUGUST/SEPTEMBER 2021
print inspection – it developed the first banknote inspection solution more than 20 years ago. Terefore, the AIT often relates its technologies back to this application, Tanner says, and uses it as a demonstrator application. Te inline computation microscopy solution makes it easy to inspect European banknotes for their fine features, printed so that blind people can feel which denomination it is.Tanner notes that the technology can also be used to inspect burrs in sheet metal production, or really any surface – glossy, matt, low texture, holograms – with tiny features that need to be reconstructed in detail. O
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