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FuDU transforms uncertainty into a strategic asset, enabling real-time defect detection with unprecedented reliability in industrial applications.
MAOL outperforms existing methods in fine-grained defect grading by effectively integrating morphological cues and adaptive thresholds, achieving robust performance even with noisy data.
A new benchmark reveals that existing defect detection methods falter in novel production scenarios, highlighting the urgent need for severity-aware assessments in manufacturing.
