The beauty of machine vision technology consists particularly in advanced software, which introduces intelligence into the equation. Even conventional equipment grants access to complex operations, if the underlying software allows this. For example, facial recognition is based on rather simple pattern recognition technology and only the intelligent software working in the background makes the information produced by the technology truly usable.
What is modern technology like, what does it offer? Is everything possible with machine vision?
The most traditional and widespread examples of machine vision technology are barcode scanners, the devices introduced in stores at the turn of the 1960s and 1970s. Today, it would be difficult to imagine a store without barcodes. In industry, machine vision technology was employed in early 1980s.
As compared to modern technologies, these pioneering machine vision solutions were quite simple, expensive, and slow. As it always is with new inventions, the beginning can be slow, since the old ways are hard to let go. “We have always done it this way” is the easiest approach and solutions based on intelligent technology are not even craved or sought for.
In the beginning, the imaging methods were strongly based on electronics, and mere acquisition and installation of imaging hardware was challenging, whereas today the hardware is available to everyone. In practice, the increase in computing power has revolutionized the field of machine vision technology: In the past, the equipment could independently execute only some basic calculations with integers, whereas today, complex calculations can be processed in real time. However, only with algorithms that are more complex can the hardware be utilized to the fullest extent possible.
Intelligence at the heart of technology
Complex algorithms help utilize the hardware to the fullest.
Examples of imaging methods:
Hyperspectral cameras are based on scattering of light into small wavelength ranges, this allows detecting things that the human eye does cannot make out. There are numerous practical applications for spectral cameras in various industries, from food and pharmaceuticals to heavier industries. For example, a hyperspectral camera can be used to distinguish between materials that look the same on the outside, as well as to examine the behavior of materials under different conditions.
Deep learning means that the machine is capable of learning things on its own and utilizing the things learned in practice. Deep learning can be utilized in facial recognition, for example. In the past, it was necessary to know exactly what features are to be measured and classified, whereas today machines can decide on their own about the things measured and make classifications. This means that a machine no longer needs to be told by a man what to check; the machine is smart enough to do the checking on its own. Ready-made algorithms are available for many measurements, but if something entirely new is on the wish list, production of the data desired requires professional skills and creation of a suitable algorithm,
3D measurement and modelling:
Utilization of 3D technology is no longer merely a popular trend; it is already used extensively for a wide range of needs. Some of the first applications of 3D measurement were log truck frame measurements, which used to be performed manually using measuring instruments.
Laser equipment were developed for the purpose, which automatically measured the volume of timber loaded onto trucks and provided information on the size of individual trees.
However, we have come a long way since those days, and modern 3D measurements are very accurate and detailed. 3D technology is utilized extensively for a variety of purposes and the size range of objects to be measured can vary from one extreme to the other, from micrometers to kilometers. A subtype of 3D measurement is stereoscopic measurement: two or more cameras are employed to produce accurate information on depths and distances. The application possibilities of 3D measurement and modelling are practically limitless; they are utilized in everything from spatial planning to observation of car tire tread.
From technological development to application of hardware and intelligence
Laser scanner in it’s natural habitat: industrial area.
Since modern machine vision technology enables entirely novel solutions, the ability to choose the solution that best suits the challenge at hand is of paramount importance. Modern machine vision technology allows truly accurate and detailed imaging and analysis with high-quality algorithms, optics, and high computing power, but there are plenty of options available. The next challenge will be finding the ways for utilization and application of these modern solutions in different environments – the technology can no longer be expected to develop very extensively, at least not in such great leaps as until now.
Today’s exciting innovations will be commonplace tomorrow, as the barcode reader, for example, has become something we all take for granted. It is also likely that algorithms will develop in the same way mobile phone applications have. In the past, each phone had its own, predetermined functions, whereas today everyone can download ready-made add-ons to complement the phone’s own features. This way, we adapt our phones to our specific needs and personalize them. The same applies to machine vision technology: You can have a limitless number of applications, but how to ultimately adapt and use them?
When utilizing machine vision, the imaging itself is often quite simple. Put simply, anyone can acquire an imaging device and use it for simple measurements completely independently. However, harnessing the full potential of machine vision technology requires professional skills and experience in a variety of applications. Sufficient lighting is of special importance for reliable performance of measurements.
Extensive possibilities for machine vision in the future as well
Comprehensive observation of the environment and automatic operation have been among the development objectives for a long time, and we will see more advanced solutions step by step.
Development and popularization of self-driving cars and forest machines is among the greatest challenges – and objectives – of machine vision. Even though machine vision technology applications are gradually becoming more familiar and closer to everyday life, there is still a lot of room for growth and specialization in the industry.
We at Pinja believe that machine vision professionals will play an increasingly important role today and in the future through continuous creation of more complex solutions to new types of challenges and demanding conditions. The need for complex algorithms will endure in the future as well, since the possibilities offered by machine vision technology will be recognized in new environments and different industries, but the demand for adaptation of hardware and technology to the existing needs will continue to increase.
I’m in project sales on the Industry Solutions business area at Pinja. In my work, I want to develop innovative solutions to make business operations more efficient and improve their quality - together with the customer. In my free time, I provide a normal and safe foster home life, boat and build a steel boat.
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