Wrinkle Detection AI

Add a real-time inspection layer to every headliner, seat cover, door panel, and instrument panel on the line with Vision AI for wrinkle detection.

Wrinkles, creases, and bridging on soft trim:

• Detect wrinkles, creases, fold lines, and bridged fabric on headliners, door panels, armrests, consoles, and instrument panels, including low-contrast wrinkles in dark fabrics and textured leathers that rules-based machine vision struggles to threshold.
• Flag loose material in recesses, map pockets, and tight-radius corners where wrap tension is lowest and defects cluster.
• Run inspection on the wrap station or laminating press outfeed so a bad part is pulled before it moves to trim and assembly.

Short fabric, cut lines, and perimeter defects on headliners:

• Measure fabric coverage against the substrate edge and flag short fabric, exposed substrate, and cut-line deviation on headliner perimeters and sunroof cutouts.
• Combine detection with a dimensional check so the same camera catches both a wrinkle in the field and a 3 mm short edge at the A-pillar.
• Feed defect location and type back to the trim cell so process engineers can trace a run of perimeter defects to a die, a fixture, or a fabric lot.

Deployment, traceability, and OEM audit readiness:

• Deploy on the industrial cameras and edge hardware already on the line, or add fixed lighting and a camera to a manual audit booth, without a separate hardware vendor.
• Log every inspection with image, defect class, and pass/fail so quality teams can answer an OEM containment request with data instead of a sort report.
• Retrain in-house as new fabrics, grains, and colors launch, so the same model covers the next program without a re-integration project.

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Frequently asked questions

What is wrinkle detection with Vision AI?

Wrinkle detection with Vision AI uses camera images and a trained computer vision model to find wrinkles, creases, bridging, delamination, and short fabric on wrapped, laminated, or sewn surfaces such as automotive headliners, seat covers, door panels, and instrument panels, as well as textiles, packaging film, and laminated composites. The model is trained on your own parts and your own defect definitions, then runs in real time at the wrap station, laminating press, or end-of-line audit so every unit is inspected, not only a sampled lot. Results, images, and pass/fail decisions are logged so the inspection can be documented under IATF 16949, ISO 9001, and PPAP.

Can Vision AI detect low-contrast wrinkles on dark fabric and grained leather?

Yes. A deep-learning model trained on your parts learns the difference between the fabric's normal texture and an actual crease, and pairing that model with consistent angled or diffuse lighting makes the wrinkle visible to the camera on every cycle.

Does wrinkle detection support IATF 16949 and PPAP?

Yes. IATF 16949 (the automotive quality management standard built on ISO 9001) expects documented, controlled, and monitored inspection processes with traceable results, and PPAP (Production Part Approval Process) expects you to show the OEM that your control plan catches the defects it names. Roboflow serves as the inspection engine: it runs the model, records every image and decision, and exports the data your quality team needs for the control plan, MSA studies, and PPAP submission. Your quality and customer-quality teams keep ownership of the acceptance criteria, defect definitions, and reaction plans, including any customer-specific requirements from the OEM.

Can it integrate with our MES, quality system, and line PLCs?

Yes. Roboflow exposes inspection results over REST, MQTT, and OPC UA, and can write directly to a database, so pass/fail and defect data flow into MES and quality platforms such as Plex, SAP, Ignition, AVEVA, and Sparta TrackWise. Where a fail needs to stop a conveyor, light a stack light, or divert a part, the result can drive a PLC output on the wrap station or laminating cell. Every decision is stored with its image, timestamp, and model version, which gives your team the traceability an OEM audit or 8D investigation asks for.

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