Region-Aware Multi-View Inspection Using Deep Learning and Domain-Informed Decision Fusion

Key points

• This paper presents a region-aware, dual-model inspection framework that combines instance segmentation of localized seat-region defects with image-level classification of global shaft-region anomalies.1
• Rather than treating these two model detections as sufficient evidence for component rejection, a deterministic decision fusion layer that formalizes industrial Standard Operating Procedures is applied to the model outputs to make the final component level decision.1
• A PLC-synchronized imaging system acquires 18 seat and 18 shaft images over a full 360-degree rotation to provide multi-view circumferential coverage.1
• The results demonstrate that formalized manufacturing acceptance criteria can complement sensitive visual models by translating frame-level detections into practical and traceable component-level decisions.1

Sources (1)

• [1]Region-Aware Multi-View Inspection Using Deep Learning and Domain-Informed Decision Fusion
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 08:29 AM

  Automated optical inspection of precision components with tiny surface defects remains challenging when different regions of the same component exhibit different defect characteristics and, therefore, different acceptance criteria.

  This paper presents a region-aware, dual-model inspection framework that combines instance segmentation of localized seat-region defects with image-level classification of global shaft-region anomalies.

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