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MAOL: Morphology-Aware Ordinal Learning for Fine-Grained Industrial Defect Severity Grading

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Fine-grained defect severity grading is essential for industrial inspection, yet remains challenging due to the ordinal nature of severity labels, the strong dependence on morphology-related cues, and the train-test discrepancy between clean annotated instances and noisy predicted instances in two-stage pipelines. We propose MAOL, a Morphology-Aware Ordinal Learning framework for fine-grained industrial defect severity grading. MAOL formulates severity grading as an instance-level ordinal learning task, incorporates explicit morphological features to enhance representation learning, introduces

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