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AI visual quality inspection for production lines

Inspect every unit at line speed for visible defects — and keep an image record of each decision.

AI visual quality inspection uses cameras and deep-learning models to detect visible defects such as scratches, dents, missing components or wrong labels on products, and to classify units as OK or not OK.

Evaluate your camerasDiscuss a pilot

The problem

Manual visual inspection is sampled, tiring and inconsistent between shifts. Escaped defects lead to customer complaints and returns; over-rejection creates unnecessary scrap.

Rule-based machine vision works well for fixed, repeatable checks but struggles with natural variation in materials, lighting and product variants.

How it works

  1. 1Defect types are defined with your quality team and sample images are collected.
  2. 2Camera position, optics and lighting are selected for the smallest defect that matters.
  3. 3A model is trained and validated on images it has not seen before.
  4. 4Results are connected to the line: reject signals, operator alerts and records in MES/ERP.

Existing IP cameras / NVR

RTSP / ONVIF streams

Edge device on-site

AI models + rules; video stays local

Events

Time, camera, zone, evidence frame

Dashboard, alerts, API

Notifications, relays, REST/webhook

Typical on-premise deployment. Cloud and hybrid options are also available.

What can be detected

  • Surface defects (scratches, dents, stains, cracks)
  • Assembly errors (missing or misplaced parts)
  • Label and print errors
  • Defect trends by line, product and shift

Typical scenarios

  • End-of-line inspection
  • In-process inspection after critical steps
  • Incoming goods inspection
  • High-variation products such as textiles, castings or natural materials

Deployment options

On-premise / edge

Analysis runs on hardware inside your network. Video does not leave the site; only events and metrics are stored or shared.

Cloud

Suitable for distributed, low-camera-count sites where central management matters more than keeping video on-site.

Hybrid

Video is processed on-site, while events, dashboards and multi-site reporting are managed centrally.

Working with existing cameras

  • Unlike safety use cases, inspection often needs dedicated industrial cameras and controlled lighting.
  • The required resolution follows from the smallest defect size and the field of view.
  • Most IP cameras and NVRs that provide RTSP or ONVIF streams can be connected, regardless of brand.
  • Before a pilot, sample footage from each candidate camera is reviewed for resolution, angle, distance and lighting.
  • Where a view is not suitable, the recommendation is usually to reposition the camera or add one — not to replace the whole system.

Integration

  • PLC signals for rejection
  • REST API to MES/ERP
  • Image archive for traceability

Privacy and security

  • No face recognition is used in safety and operations analytics; events describe what happened, not who.
  • On-premise processing keeps video inside your network.
  • Evidence clips, retention periods and user access are configurable and should follow your GDPR/KVKK policies.
  • Workplace monitoring should be introduced with clear employee information and, where applicable, consultation with employee representatives.

Limitations

  • Defects that are not visible to the camera (internal, behind surfaces) cannot be detected.
  • Rare defects may need anomaly-detection approaches and more validation time.
  • Escape and false-reject rates are measured separately; both matter.

How a pilot works

  1. 01

    Discovery

    We agree on the problem, the owner, the sites and what a successful outcome would look like.

  2. 02

    Camera assessment

    Sample footage from candidate cameras is reviewed for angle, resolution, distance and lighting.

  3. 03

    Use-case definition

    Events, zones, rules, alert recipients and success metrics are written down before deployment.

  4. 04

    Pilot deployment

    Edge hardware or cloud processing is set up and connected to the selected cameras.

  5. 05

    Validation

    Detections are checked against reviewed footage; true detections, false alarms and misses are counted per camera.

  6. 06

    Report

    Results, limitations and recommended changes are documented and shared with all stakeholders.

  7. 07

    Rollout decision

    Based on measured results, you decide whether and how to expand to more cameras, sites or use cases.

Frequently asked questions

How many defect images do we need?

It depends on the defect type and variation. Common defects may need hundreds of examples; for rare defects, anomaly detection trained on good parts is often used.

Can it keep up with our line speed?

Line speed is measured during discovery and camera, triggering and edge hardware are chosen accordingly.

Related solutions

Production Line MonitoringSee when a line is running, stopped or slowed down — and why — without waiting for end-of-shift reports or wiring every machine.Anomaly DetectionFlag what does not look normal — on products, in processes or in areas — when every possible problem cannot be defined in advance.Product CountingTurn camera views of conveyors, stations and docks into reliable counts — per shift, per line, per shipment.

Industries

ManufacturingJewelry & Precious Metals Manufacturing

Case studies

Scrap rate decline through early defect detection on a conveyor lineHome-appliance component manufacturer — TürkiyeHow Line A scrap rate dropped 31% in packagingMid-sized apparel and packing facility — Türkiye

Further reading

How Is Accuracy Measured in Computer Vision? False Positives and False NegativesHow Does AI Quality Inspection Work?

Start with the cameras you already have

Send us a few frames from your cameras and the problem you want to solve. We will tell you honestly what is feasible.

Evaluate your camerasDiscuss a pilot