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We propose a multi-agent based vision solution that enables real-time quality inspection under high-throughput image streams from multiple cameras and line scanners. The system targets production environments with tight bandwidth and latency budgets, ensuring that frames are ingested, processed, and judged without bottlenecks as sources scale.
The solution is modular and comprises six key stages: first, an orchestration agent for high-speed frame acquisition and buffering; second, lightweight preprocessing including ROI selection, resizing, denoising, and illumination normalization; third, delivery to a model-based inference engine; fourth, pass/fail determination using adaptive thresholds and hysteresis; fifth, persistent storage of inspection results and metadata; and sixth, operator-facing verification via a user interface. Each stage is encapsulated as a dedicated agent, enabling load balancing, backpressure control, and automatic CPU/GPU pool scaling.
For the inference stage, we adopt unsupervised learning and select one model per deployment without ensembling: either PaDiM for computation-light, real-time edge deployments, or Dinomaly for stronger generalization to emergent defect modes in multi-class environments. |
IEEE/ICACT20260190 Slide.10
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