Machine vision has become an essential pillar for optimising industrial processes, especially in quality control. However, not all solutions are equal. The key difference between traditional systems and those based on artificial intelligence (AI) lies in their ability to adapt and learn, which has a direct impact on the efficiency and precision of the process.
(If you are looking for a step-by-step framework to decide between the two, we have a dedicated guide for that: Choose the ideal machine vision system for your company. Here we focus on how we experience it working with real clients.)
What is traditional machine vision?
Traditional machine vision relies on predefined rules designed to detect specific features in images, such as edges, colours or sizes. Although these rules are effective in controlled environments, they are incapable of evolving when faced with unexpected environmental changes or variations.
Advantages:
Mature technology: it has been used successfully for decades in highly stable environments.
Initial simplicity: its configuration is relatively straightforward for well-defined problems.
Limitations:
Lack of adaptability: it does not respond well to changes in lighting, shadows, reflections or variations in inspected products.
Limited precision: its effectiveness decreases when facing noisy images or unforeseen disturbances.
No continuous learning: it does not improve over time nor can it adapt to new defects.
What does AI-based machine vision offer?
AI, and deep learning in particular, has transformed machine vision by enabling systems to learn complex patterns directly from data. This makes them more precise, adaptable and prepared to face a wider variety of industrial challenges.
Advantages:
Flexibility: it responds effectively to changing conditions, such as lighting fluctuations or product variations.
Constant evolution: the more it is used, the more its precision improves.
Versatility: it is capable of solving a wide range of problems, from defect detection to component classification.
Practical case study: imagine a production line where defects include small cracks on reflective surfaces. While a traditional system might fail to identify them (glare and reflections confuse an algorithm based on fixed contrast rules), an AI model trained on specific data can adapt and reliably detect them.
How Rely addresses these differences
As an AI-based visual inspection platform, at Rely we go beyond the capabilities of traditional systems:
Full automation: we process images in real time and respond instantaneously to prevent defects from reaching the end customer.
Comprehensive traceability: we store images and data associated with each part during the process, allowing customer-supplier disputes to be resolved objectively (the same principle we explained in the €70,000 claim case study in Is improving quality control profitable? Cases and figures).
Continuous improvement: we leverage retraining capabilities to incorporate new defects or variations without needing hardware modifications.
Technical comparison: traditional vs. AI
Aspect | Traditional vision | AI-driven vision |
|---|---|---|
Adaptability | Limited to predefined conditions | Responds to lighting changes and new variations |
Precision | Only in controlled environments | High even in complex conditions |
Evolution capacity | Static, no improvement over time | Constant improvement thanks to continuous learning |
Applications | Specific and simple problems | Flexible, from defect detection to advanced classification |
The key role of technical education
Many clients arrive with doubts about these technologies due to bad prior experiences or mismanaged expectations. At Rely, we invest time in educating our clients on the specific benefits of AI. This includes:
Personalised demonstrations on their own processes, not on generic cases.
Explanation of real use cases, such as those included in our project history.
Technical training on how to leverage Rely for continuous improvement, not just on launch day.
This part of the process matters just as much as the technology itself: an AI system misunderstood by the team using it daily ends up underutilised, no matter how good the underlying model is.
Frequently asked questions
Does traditional machine vision become obsolete against AI?
No. It remains the most efficient option for simple, well-defined tasks in stable environments. AI adds value when there is variability, not as a universal replacement.
How long does it take for a client to adapt to working with an AI system?
It varies depending on the team, but that is why we dedicate specific time to training and demonstrations on their own process, not just to technical installation.
Does Rely completely replace manual inspection from day one?
No, the system is trained and validated against the criteria of the existing quality team before operating autonomously, as we explain in Quality errors that machine vision detects (and you don't).
What happens if a type of defect appears that the system has never seen?
It is incorporated into the retraining cycle without needing to touch the hardware, which is one of the advantages over a traditional system, where rules would have to be manually reprogrammed.
Conclusion: innovation through AI
The choice between traditional vision and AI-based vision is not just a matter of technology, but of strategic vision. With Rely, factories do not just acquire a quality control tool, but a technology partner that evolves with their needs and allows them to remain competitive.
Would you like to see how it would fit into your process?
We will show you a demonstration on your own case, not a generic one.