The Limits of Traditional Machine Vision
Updated: Nov 29, 2021
Damian Heimel from deevio explains in which cases traditional machine vision reaches its limits and why it is not too late for companies that have relied on such a system and are dissatisfied with the result to switch to machine vision with deep learning.
Can you start by explaining what traditional machine vision means and how it works?
Machine vision means image processing in the industrial sector. It's all about automating visual inspections, especially within the production line. For this purpose, cameras are integrated into the production line, images are taken and appropriate software is created. Until now, software development has been rule-based. You can imagine it this way: You have the image in front of you and define very specific rules for it. For example, that a scratch always goes from top left to bottom right or that the distance should always be 55 mm. Machine vision is already being used very frequently within the production line, which works very well. There are probably hardly any production lines left where this is not the case. Here, attention is normally paid to smaller defects in parts of the product at several points on the production line, and less frequently to entire products.
Then why are there so many companies that are sceptical about machine vision? Where does the dissatisfaction come from?
This is strongly dependent on the application. We would continue to recommend our customers to use rule-based machine vision for tasks such as measuring distances. However, there are some aspects that are difficult to cover with traditional image processing methods. These include, for example, the detection of scratches or dents.
Many companies have already invested in machine vision systems with very good hardware, but with a software that is not able to cover the variability of errors mentioned above. These companies have noticed that the systems show disproportionately high pseudo error rates, some of which are 30-50 percent.
One reason for this is that the machine vision systems are often very sensitive and thus react strongly to small deviations. This can be an incoming beam of light or someone who accidentally hits the machine and thereby changes the camera angle. The resulting pseudo defect rate means that the inspection costs are not reduced as expected and the expensive systems are basically worthless. Instead, despite the investment in the system, the products have to be tested several times to prevent defects, which ultimately drives up the costs of the products. Therefore, many companies are sceptical about traditional machine vision systems.
What can these companies that have already invested in a machine vision system do?
We at deevio can help these companies. What we can do is that we train a properly functioning deep learning model with the images from the already installed system, which have a very good image quality. We install the deep learning model on one of our AI boxes consisting of graphics card and minicomputer and integrate it into the existing system. As our software learns, it is more flexible and can handle some use cases better. This allows us to reduce the pseudo error rate from 50 to up to 1 percent. Hence, we are able to retrofit the already existing system.
Why should a company invest in your solution when it already has a machine vision system?
On the one hand, because it is a relatively simple and uncomplicated process for the company. The hardware remains the same. Only the software is replaced. On the other hand, because the company can save the already spent investment costs and, in the end, has a working machine vision system that actually reduces the inspection costs.
In the next interview Damian describes the individual steps a company goes through if it wants to automate its final quality control with the help of deevio.