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Volume Estimation

Accurate Volume Estimation with AI-Powered computer vision

What It Does


This solution continuously monitors material flow on conveyor belts or in bunkers, calculating volume per time unit with high accuracy. It enables:

  • Real-time volume tracking

  • Accurate fill-level estimation

  • Early detection of anomalies or blockages

  • Data-driven operational decisions


Actual volume statistics, live on multiple conveyor belts
Actual volume statistics, live on multiple conveyor belts

How It Works


  • AI Vision Models: High-resolution cameras capture the waste stream, and AI models segment and analyse the material to estimate volume based on surface area, sharpness, and movement.

  • Multi-Camera Support: The system supports multiple camera angles and configurations to ensure full coverage and redundancy.

  • Live Dashboards: Volume metrics are visualised in real time using platforms like the Viu More AI Vision Platform, enabling operators to monitor trends and react quickly.

  • Edge or Cloud Deployment: The solution can run on local GPU/IPC infrastructure or be integrated into cloud-based environments.

Measure more. Waste less. Our Volume Estimation solution uses advanced computer vision and AI to deliver real-time, high-precision volume calculations - empowering waste operators to optimise throughput, bunker fill levels, and resource planning.

Why It Matters


  • Optimised Logistics: Know exactly when to empty bunkers or dispatch transport.

  • Improved Efficiency: Reduce manual checks and avoid overflows or underutilisation.

  • Scalable Architecture: Easily expand to new lines, sites, or material types.

  • Data-Driven Insights: Use historical data to improve planning and forecasting.


Built for Industrial Environments


Our solution is robust, modular, and designed for real-world waste processing conditions. It supports:

  • Integration with process control systems and other AI modules (e.g. quality, contamination, object detection)

  • Customisable thresholds and alerting

  • Operator training and support for smooth adoption

  • Continuous model improvement through active learning

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