Where can image recognition be effectively applied?

Where it works well

• Counting parts on a pallet
• Completeness of a picking order
• Reading labels – type plates, batch numbers
• Detecting obvious deviations – missing parts, wrong colors
• Damage documentation during handover or return

What is difficult

• Fine quality defects such as hairline cracks
• Highly variable lighting
• Reflective or transparent surfaces
• Rare error images – a system learns little from examples that are hardly available

The last point is the most common reason for disappointed expectations: it is precisely the rare errors that one wants to detect, and for which the data is lacking.

What determines success

The recording situation. Fixed camera position, even lighting, defined background. Investing here saves multiple times the effort in model development.

A poor recording cannot be compensated for by any model.

Data protection

As soon as individuals can be in the image, the GDPR applies. In production and storage areas, camera surveillance of workplaces is subject to co-determination and is only permissible under strict conditions.

Technically solvable by cropping images that do not capture individuals or by pixelation before processing.

Approach

First, conduct a test with real images from your environment, then decide. Demonstrations by the provider take place under ideal conditions.