Artificial intelligence can be part of a machine vision solution, but using it is not automatically necessary. Instead of starting with the technology, it is worth starting with the objective: what needs to be determined from the image, and how far should that information be used?
When designing a machine vision solution, AI may seem like a natural choice even when the task does not necessarily require it. If the required information can be determined computationally from the image, using AI does not in itself make the solution more appropriate. The key is to assess what the task actually requires in practice. The implementation does not need to be as technically advanced as possible if a simpler method can achieve the desired result.
This also matters when considering the scope of the implementation. Using AI may require hardware investments and the development of a training process – and the work does not end when the system is deployed. A trained system also needs to be monitored and updated when necessary. The chosen implementation method therefore affects the amount and type of work the solution will require in the future as well.
When assessing whether AI is necessary, it is therefore worth considering not only what is required to implement the solution, but also what will be required to maintain it later on.
If the same objective can be achieved computationally without AI, the additional steps required by an AI-based solution may become unnecessary costs and sources of delay. The question is therefore not about choosing the most advanced technology available, but about finding an implementation that is appropriate for the task.
From Detecting Defects to Understanding Their Causes
The value of machine vision can extend beyond detecting a defect. At its best, the information obtained from an image can also help determine what caused the detected defect in the first place. In this case, the analysis does not stop at identifying an individual deviation, but can provide information about the factors behind it.
This difference can have a significant impact on what machine vision ultimately achieves. Detecting a defect tells us that something does not meet the desired result. If the image can also provide information about where the defect originated or what caused it, that information can be used to prevent the problem itself. The objective is then not only to identify a defect that has already occurred, but also to better understand the factors that led to it.
The role of machine vision can therefore extend from observation to prevention. When enough information is available about the cause of a defect, it may be possible to find ways to prevent the same defect from occurring altogether. In this case, the value of the image data increases: the observation is not only an end result, but can also be used to investigate the background of the problem.
The key question is therefore how far the information obtained from the image needs to be used. Is information about the deviation enough, or is the objective also to understand what caused it?
The objective defined for the solution largely determines what needs to be identified from the image. In one application, detecting a predefined feature or deviation may be sufficient. In another, the image may also need to provide information about the factors behind the detected defect. This, in turn, affects what kind of implementation is required.
The possible role of AI is also determined by this objective. Its use should be evaluated in relation to what information is needed from the image and how that information will be used. This ensures that the choice of technology is based on the task that needs to be solved rather than on the technology itself.
→ What kind of machine vision solution best meets your company’s needs?
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