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

MAOL (Morphology-Aware Ordinal Learning) is a new framework for fine-grained industrial defect severity grading. It treats severity grading as an instance-level ordinal learning task, uses explicit morphological features to improve representation, introduces class-conditional adaptive ordinal thresholds to capture defect-specific grading boundaries, and applies prediction-aware training with localization perturbation to handle noisy predicted instances. Experiments show MAOL outperforms rule-based methods, nominal classification models, and existing ordinal baselines, especially in predicted-instance settings, and it ranked third in the IDA 2026 Challenge on Fine-Grained Severity Grading for High-Precision Manufacturing.