Trustworthy Visual Quality Inspection under Data Scarcity in Manufacturing
Automated visual inspection in manufacturing aims to replace slow and inconsistent manual checks, but its economic value depends on whether its decisions can be trusted enough to automate routine inspection while reserving human expertise for ambiguous cases. In production-line settings, defective samples are scarce, since the process is optimized to produce good parts, which limits any learning-based inspector trained on real data alone. Compounding this, defect decisions emitted as hard labels with no confidence estimate carry an asymmetric cost: a false reject wastes a good product, while a false accept may increase the risk of undetected defects progressing through the production proc...
| Authors | Panagiotis Sapoutzoglou, Jessy Ribaira, Martin Kanounnikoff et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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