Which is the best option for your company?
Choosing the right machine vision system for your company can make all the difference in the quality and efficiency of your processes. To do so, key factors to analyse include environment variability, required accuracy, and available budget.
If you operate in a stable environment with uniform parts and repetitive processes, a traditional machine vision system may be the most efficient and cost-effective option. However, if your process requires adaptability to changes, a high level of accuracy, and flexibility, AI-powered machine vision systems will be the best solution. Even in stable environments, if you are looking to ensure optimal performance and optimise long-term efficiency, these systems can make all the difference.
Furthermore, integrating these technologies not only improves the quality and efficiency of your processes, but also delivers a competitive advantage in a constantly evolving sector.
A bit of context
Before choosing the right system, it is essential to understand what machine vision is and how it can be applied in different scenarios.
Machine vision is a key technology in industrial automation, enabling high-precision inspection, quality control, and process monitoring. In recent years it has advanced significantly, driving improvements in efficiency, productivity, and competitiveness across industry. Thanks to this evolution, industrial systems do not merely "see", but analyse and make autonomous decisions, optimising processes like never before.
Currently, there are two main approaches: traditional machine vision systems and artificial intelligence-based systems. Choosing between them depends on the needs of each company, the type of defects to detect, the required flexibility, and the available budget.
Traditional machine vision system
These systems rely on predefined rules and image processing algorithms, which is why they are also known as rule-based vision systems.
They operate by applying filters, thresholds, or edge detection to analyse images and make decisions based on specific, pre-programmed parameters.
They are particularly effective for predictable tasks and controlled environments, where environmental conditions and the characteristics of the inspected objects remain constant. However, any variation in the field of view (changes in lighting, position, or object shape) can significantly affect their performance.
Common applications include quality inspection of parts with uniform characteristics or barcode reading.
AI-powered machine vision system
Vision systems based on Deep Learning models overcome many of the limitations of traditional systems. Certain tasks are too complex, or even impossible, to develop using conventional algorithms, but artificial intelligence has enabled vision systems to reach unprecedented levels of accuracy and adaptability.
Unlike traditional systems, AI machine vision systems use machine learning models and neural networks to analyse and classify images. Instead of relying on fixed rules, they learn patterns from large volumes of data, allowing them to adapt to real-time changes. For example, they can detect defects in irregularly shaped parts, complex textured surfaces, or variable lighting conditions, without the need for constant manual adjustments.
One of their greatest advantages is their capacity for continuous improvement: as they receive new data, they become more accurate and efficient over time.
Furthermore, artificial intelligence has expanded the impact of machine vision systems beyond the industrial realm, with applications in sectors such as healthcare, sports, the food industry, and underwater exploration.
Traditional vs. AI at a glance
Traditional vision (rule-based) | AI-powered vision | |
|---|---|---|
How it works | Manually programmed filters, thresholds, and edge detection | Deep Learning models trained on real images |
Best for | Stable environments, uniform parts, repetitive processes | Variable environments, irregular parts, high precision required |
Sensitivity to changes | High: different lighting, position, or shape affects the outcome | Low: adapts to variations without reprogramming rules |
Improves over time | No, except through manual reprogramming | Yes, improves with every new data point received |
Typical initial cost | Lower | Higher, but with a better return on investment in complex processes |
Example use case | Barcode reading, homogeneous parts | Detection of defects such as pores or cracks on complex surfaces |
How to choose: three questions to help you decide
Before committing to one system or another, it is worth answering the following:
How much variation is there in what is being inspected? If parts are practically identical to one another and the environment does not change, traditional vision will likely be sufficient and more cost-effective.
How much does an undetected failure cost? The higher the risk of a quality claim or critical defect, the more justifiable the additional investment in an AI system with greater adaptability (you can see real-world examples of this calculation in Is it profitable to improve quality control? Cases and figures).
Will the process change in the future? If you anticipate new part numbers, product updates, or growth, an AI system adapts without having to reprogramme rules from scratch each time.
In many projects, the answer is not "one or the other": we start with traditional vision on the most stable lines and add AI for the most critical or variable part numbers, following the same phased logic we explain in Five common mistakes in quality control.
Frequently asked questions
Is an AI system always better than a traditional one?
Not necessarily. In a highly stable environment with uniform parts, a traditional system can be just as effective and more economical. AI provides the greatest value when there is variability or defects that are difficult to define using fixed rules.
Can traditional machine vision and AI be combined in the same plant?
Yes, it is common practice. Many companies use traditional vision for simple tasks (such as code reading) and AI-powered vision for more complex defect inspection.
How long does it take for an AI system to be ready for production?
It depends on the volume of data available and the complexity of the defect to be detected. The system improves with each training and validation cycle until it achieves the accuracy needed to operate autonomously.
What if my process changes in a few years?
That is precisely where AI makes the difference compared to traditional vision: it adapts by learning from new data, rather than requiring rules to be reprogrammed from scratch.
Conclusion
Choosing the right machine vision system is not just a technical issue, but a strategic decision. If your production environment is stable and you are looking for an efficient, cost-effective solution, traditional vision systems may be the best option. But if you require greater accuracy, flexibility, and adaptability, artificial intelligence takes machine vision to another level.
In any case, what matters is that the technology adapts to your company, and not the other way around. Investing in machine vision not only optimises the quality and performance of your processes, but also prepares you for the future in a world where automation will make all the difference.
Which vision system is the best fit for your company?
Tell us about your process today and we will help you choose between traditional vision, AI, or a combination of both.