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iQsolutions

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Künstliche Intelligenz

Maschinelles Sehen

Bildverarbeitungssysteme, die in Bildern und Videos erkennen, prüfen, zählen und lesen – in der Cloud oder am Edge betrieben, mit besonderem Augenmerk auf Genauigkeit, Datenschutz und die Bedingungen vor Ort.

Projekt startenMit Experten sprechen

Überblick

Cameras are already installed on production lines, loading docks, vehicles and retail floors. Computer vision turns that footage into structured events: a defect found, a pallet counted, a safety zone breached, a label read.

Success depends as much on optics, lighting, data collection and edge hardware as on the model itself. We engineer the full system, from image capture and annotation to model training, edge deployment and the integration that turns a detection into an action, and we validate performance under the real conditions where it will operate.

Geschäftliche Anwendungsfelder

  1. 01

    Visual quality inspection

    Detect surface defects, assembly errors and missing components on production lines, flagging items for rejection or manual inspection and retaining images to support root cause analysis.

  2. 02

    Logistics and yard operations

    Read container numbers, license plates and labels, count and classify goods at receiving docks, and track trailer and asset movements to keep inventory and yard management systems current.

  3. 03

    Workplace safety monitoring

    Identify protective equipment use, restricted zone entry and unsafe proximity between people and vehicles, alerting supervisors in near real time, with privacy measures designed in from the start.

  4. 04

    Retail and field asset analytics

    Monitor shelf availability and planogram compliance in stores, or analyze images captured by field technicians and drones to assess the condition of equipment, infrastructure and installations.

Engineering-Ansatz

  1. 01

    Study the scene

    Assess camera placement, resolution, lighting, motion and environmental conditions on site, because these physical factors often limit performance more than the choice of model does.

  2. 02

    Build the dataset

    Collect representative images across shifts, seasons and edge cases, define clear annotation guidelines, and label data with quality review to produce reliable training and test sets.

  3. 03

    Train and benchmark

    Fine-tune detection, segmentation or classification models, and benchmark accuracy, latency and failure modes on a held-out test set captured under real operating conditions.

  4. 04

    Deploy at the right tier

    Run inference on edge devices, on-premises servers or the cloud according to latency, bandwidth and privacy requirements, using optimized models with remote update capability.

  5. 05

    Integrate and monitor

    Send detections to MES, WMS, safety or maintenance systems as structured events, and monitor accuracy over time as cameras, products and site conditions change.

Technologien und Plattformen

Models and libraries

  • PyTorch
  • Ultralytics YOLO
  • Detectron2
  • OpenCV
  • Segment Anything
  • Hugging Face Transformers

Edge and inference

  • NVIDIA Jetson
  • NVIDIA DeepStream
  • ONNX Runtime
  • TensorRT
  • OpenVINO

Data and annotation

  • CVAT
  • Label Studio
  • Roboflow
  • FiftyOne

Platform and integration

  • Kubernetes
  • AWS IoT Greengrass
  • Azure IoT Edge
  • MQTT
  • Apache Kafka

Technologienamen sind Marken ihrer jeweiligen Inhaber und implizieren keine Partnerschaft oder Empfehlung.

Was Sie erhalten

  • Site assessment covering cameras, lighting and infrastructure
  • Annotated dataset with labeling guidelines and test set
  • Trained models with benchmark results under real conditions
  • Edge or cloud inference deployment with remote update capability
  • Integration with operational systems and alerting
  • Privacy considerations and data retention configuration

Häufige Fragen von Entscheidern

Can we use our existing cameras?

Often, but not always. Existing cameras may lack the resolution, frame rate, angle or lighting a specific task needs. We assess your current installation early and test models on footage from it. Where hardware changes are needed, we specify them and explain the reason before you invest in new equipment.

How do you handle privacy in video analytics?

We design for privacy from the outset: processing at the edge where possible, blurring or discarding faces when identity is not required, limiting retention and restricting access to footage. Applicable laws differ by jurisdiction and use case, so we work with your privacy and legal teams to confirm requirements before deployment.

What if the model makes a wrong detection?

Every vision model produces some false positives and misses. We measure these rates on your data, tune thresholds to reflect the relative cost of each error type, and design workflows where consequential detections are confirmed by a person. Monitoring shows when performance changes so the model can be retrained.

Verwandte Lösungen

Intelligenter Industriebetrieb

Die Fertigung mit den Entscheidungen verbinden, die Ausbringung, Qualität und Verfügbarkeit bestimmen.

Vernetzte Flottenintelligenz

Signale von Fahrzeugen und Anlagen in Entscheidungen verwandeln, auf die Disponenten und Führungskräfte reagieren können.

Verwandte Branchen

Verwandte Leistungen

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Projekt startenMit Experten sprechen