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Computer Vision for images and videos

From the production line to the point of sale, from video surveillance to medical imaging: we bring computer vision models into production, even on edge devices with tight latency, cost, and power constraints.

Image and video recognition for quality control, security, retail, and industrial analysis.

Use cases

  • Automated quality control in production
  • People counting and retail heatmaps
  • Perimeter security and anomaly detection
  • ID document OCR and KYC
  • Sports analysis and tactical AI

Measurable benefits

  • 100% inspection (vs manual sampling)
  • Reduction of post-sale discarded defects
  • Insights into customer behavior in-store
  • Operation even offline / edge

Technical details

Models

  • YOLOv8/v9 for real-time detection
  • Segment Anything (SAM) for segmentation
  • CLIP for visual search and zero-shot
  • Custom fine-tuned models

Video pipelines

  • RTSP/WebRTC streaming
  • Multi-object tracking (ByteTrack, DeepSORT)
  • Re-identification
  • Temporal anomaly detection

Edge & deployment

  • NVIDIA Jetson, Coral, Raspberry Pi
  • INT8 quantization for latency/W
  • ONNX, TensorRT, OpenVINO
  • OTA model updates

Privacy

  • Automatic face / license plate blurring
  • On-device processing (no cloud)
  • GDPR and DPIA compliance
  • Access audit logs

How a computer vision system is built

  1. Dataset collection — We define realistic capture conditions — lighting, angle, resolution, speed — because that is where most projects fail.
  2. Labeling — We agree classes and labeling rules with domain experts, then check agreement between annotators.
  3. Choice of approach — Classification, detection, segmentation or plain geometric analysis: the simplest technique that solves the problem wins.
  4. Validation — We measure precision and recall with hard cases isolated, and define which errors the real process can tolerate.
  5. Inference — We decide where the model runs — on device or on a server — based on latency, connectivity and image confidentiality.
  6. Operational integration — The result enters the existing flow: reject, flag, stop the line, open a ticket, with human review on uncertain cases.
  7. Monitoring — Conditions change: new product, new camera, new season. Performance is reviewed periodically on recent data.

We deliver the labeled dataset, validation metrics, model weights or configuration, and documented operating conditions.

Working with us

The team that analyses the process is the team that builds and maintains it: product, engineering, integration with your existing systems, governance of automated decisions and post-release support.

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FAQ

How many images are needed to train a model?

With transfer learning, 500-2,000 labeled images per class are often enough. We also provide data labeling services.

Does it work in real-time?

Yes, with the right models (YOLO, MobileNet) we reach 30-60 FPS even on edge hardware.

Can I use my existing cameras?

Yes, any RTSP/ONVIF/HTTP stream can be integrated. We suggest upgrades only if necessary.