Omron Expands Vision System With Locally Training AI for Robust Error Detection

Industrial Image Processing Omron Expands Vision System With Locally Training AI for Robust Error Detection

2025-10-09 From Manuel Christa | Translated by AI 2 min Reading Time

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Omron Electronics Components Europe BV

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Omron is launching an AI update for its FH vision system. The new defect detection trains locally, reduces false alarms and delivers reproducible results even on reflective, structured or variable surfaces.

Cylindrical batteries during inspection(Image: Omron) — Cylindrical batteries during inspection
(Image: Omron)

In many lines today, interference patterns tip the balance: welding marks, shadows, changing textures or reflections trigger inspection systems, even though the product is in order. The update targets precisely these cases. It automatically selects the most suitable training images and uses them to build a defect model that separates real defects from visual noise. According to Omron, this means that fewer images are required for a reliable setup and the results remain consistent without the need for subsequent fine-tuning by specialists.

AI Separates Faults from Fault Patterns

The algorithm specifically distinguishes between real defects and difficult backgrounds. This addresses typical borderline cases of inline inspection, such as uneven surfaces, material transitions or reflective coatings. After training, the system delivers identical test results with identical data. This increases traceability during operation and simplifies approvals in qualified environments.

Gallery

Eight Cameras Per Controller

For more perspectives, an FH controller connects up to eight cameras that can be triggered independently of each other. This allows inspections to run simultaneously or asynchronously, for example from different angles or at several stations of an inspection station. One license covers all eight channels, which reduces administrative effort and makes scaling easier.

Commissioning follows a clear process. First, reference and error images are created that represent the spectrum of real production. Then the annotation marks the relevant points as you would show them to a colleague. The self-learning training then starts with a click. Because the selection of training images is automated, the risk of distortions from the image selection migrating into the model is reduced.

Rule-Based Plus AI

Omron combines classic, rule-based inspections with AI modules in the FH Vision System. This allows flexible strategies: Where rules take effect quickly and reliably, they remain in place. Where variability dominates, the AI takes over. The hardware is designed for continuous industrial operation. An unlimited license model ensures long-term use, without ongoing subscriptions or special programming. (mc)

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