NEON1X Detect

Analyzes. Evaluates.Identifies.

NEON1X Detect uses machine learning for automated quality control. Deep learning enables the detection of hard-to-spot defects.

Assess my applicationHow NEON1X Detect works
Deep learning Anomaly heatmap Local Edge AI

When to use it

Detecting irregular defects

Surface variations, scratches, deformations, missing parts, contamination or changes in appearance that differ from one case to the next.

Advanced AI in industry

We bring advanced neural network methods into production.

Anomaly heatmap

Visualizing the location and intensity of defects.

High throughput

Defect detection in milliseconds.

System configuration

Optimal camera, optics and lighting configuration for each defect type.

What Detect can find

Small deviation.
Big difference.

From damaged labels to incomplete packs. Explore where defects can occur and move the divider to reveal their location.

Small liquor bottle with a torn and peeling paper label.

01 / Bottles · Labels

Quality includes the label

Torn, peeling or crooked labels. Inspect front labels and fine details on small bottles.

Cardboard yogurt tray with four rows of five positions, a missing cup, a wrong variant and a damaged lid.

02 / Multipacks · Completeness

Twenty positions. Every one counts.

Check a 4 × 5 tray for completeness, correct product variants, cup positions and intact lids. A single faulty item changes the result for the entire pack.

Close-up of an HDPE sheet with a hole and a raised surface bubble.

03 / Plastic sheets · Surfaces

Surface flaws, brought to light

Holes, bubbles and local deformations in HDPE and other plastic sheets. Highlight defects on an otherwise uniform surface.

Three identical bottles with matching cap colors, one crooked cap and one damaged tamper-evident ring.

04 / Caps · Correct assembly

The right color is not enough

A cap can be the right color and still be fitted incorrectly. Check seating, tilt and the integrity of the tamper-evident ring.

Green circuit board with a central plastic component and two defective solder joints.

05 / Electronics · Solder joints

A flaw in a single joint

Missing solder, an irregular joint or a damaged pin. Inspect fine details in a small electromechanical assembly.

Fine foreign fibre on a light-colored plastic component in an electromechanical assembly.

06 / Assembly · Part cleanliness

A fibre that does not belong

Hair, fibres or other foreign particles on a part. Check cleanliness before closing the assembly, while contamination is still visible.

Close-up of a small glass bottle base with a chipped edge and a crack.

07 / Glass · Mechanical damage

A crack at the bottle base

Chipped edges, cracks or a damaged base. Inspect glass containers under lighting that reveals deviations in transparent material.

Corner of a food tray with a leaf fragment in the seal and a wrinkle in the sealing film.

08 / Food packaging · Film seals

A clean, continuous seal

Product residue in the seal or wrinkled film can compromise package closure. Visually inspect deviations along the sealing area.

AI-generated application illustrations. Defect highlights are illustrative; detection on your products will be validated through a feasibility study.

Struggling with manufacturing defects?

Send us samples so we can carry out a feasibility study and design a complete solution for you.

Submit an application for assessment