A machine vision system that fails to detect defects properly can quickly become a critical issue in production. In many cases, these limitations do not stem from the principles of machine vision itself, but from the fact that the system is not suited to the application.

It is in these situations that a customized machine vision approach becomes necessary to achieve the desired performance.

---

Signs of an ineffective vision system

There are several signs that may indicate a machine vision system is reaching its limits:

• visible but undetected defects
• results may vary depending on conditions
• excessive sensitivity to changes in lighting
• frequent adjustments required
• abnormally high rejection rate
• abnormally high false-positive rate

These issues directly impact quality, productivity, and confidence in the system. In many cases, a custom machine vision solution can achieve performance levels that would be impossible with a standard approach.

The most common causes

1. An inappropriate approach

Optics play a central role in image quality. An improper configuration can prevent defects from being detected correctly. For example, at Imasolia, we handle submicron defects, which makes the design of the optical system essential.

2. Inadequate lighting

This is often the main cause of problems. Poorly designed lighting can:

• hide flaws
• create reflections
• generate noise

We regularly use specialized lighting systems tailored to each specific application. Mastering lighting is key to identifying defects and ensuring reliable results in industrial settings.

3. Limitations related to the sensor

Here’s a more robust, fluid, and value-driven version 👇

Insufficient resolution, noise, limited dynamic range… The sensor can quickly become a limiting factor in certain applications. The selection and integration of the sensor are therefore critical to ensuring measurement quality. At Imasolia, we pay particular attention to this stage, selecting and utilizing the most suitable sensors to ensure reliable performance, even in the most demanding configurations.

4. Non-optimized image processing

Here’s a stronger, more “sales-oriented” version 👇

Algorithms must be tailored to the application in order to fully leverage the acquired data. A generic approach quickly reaches its limits as soon as conditions become complex. At Imasolia, we develop specific processing methods, customized for each use case, to ensure reliable, robust, and actionable detection in industrial environments.

Why are standard machine vision systems reaching their limits?

Commercial systems are designed to handle a wide range of applications. While this versatility is an advantage, it becomes a limitation when conditions are specific. In complex cases (such as shiny surfaces, transparent materials, or high precision), a standard approach does not always deliver the expected performance.

In many cases, a custom machine vision solution can achieve performance levels that would be impossible with a standard approach.

How can you improve a machine vision system?

Before completely switching to a different solution, there are several options that can be explored:

• review the optical configuration
• tailor the lighting to the specific needs
• improve positioning or stability
• adjust the processing algorithms
• analyze actual conditions of use

When should you switch to a custom machine vision system?

A customized machine vision solution is appropriate when:

• Standard solutions do not produce reliable results
• The inspection requirements are complex
• the results are not reproducible
• the accuracy requirements are high

In these situations, it is no longer a matter of adapting the application to an existing system, but rather of designing a solution specifically tailored to the need.

A customized machine vision approach involves optimizing the entire measurement chain—including optics, lighting, sensors, and processing—to overcome technical challenges and achieve reliable results under real-world production conditions. This approach not only improves defect detection but also reduces scrap, stabilizes performance, and enhances the reliability of quality control.

The goal is simple: to have a robust, reproducible system that can be put directly into production, even in the most demanding environments.

Conclusion

A machine vision system that isn’t performing as expected doesn’t necessarily need to be replaced. In many cases, a thorough analysis can identify the root causes of its limitations and lead to concrete improvements, sometimes through targeted adjustments.

Whether it’s optics, lighting, the sensor, or image processing, every link in the chain can be optimized to significantly improve performance. This approach often makes it possible to resolve complex issues without having to start from scratch.

When a standard solution reaches its limits, a more tailored approach can transform an unstable system into a reliable, usable, and sustainable tool for production.

Custom Machine Vision: Taking It to the Next Level

The right approach allows you to quickly identify areas for improvement and achieve reliable production performance. If your machine vision system isn’t delivering the expected results, a quick analysis can help identify areas for improvement.