针对工业缺陷严重度分级难题，提出形态感知序数学习框架。
MAOL: Morphology-Aware Ordinal Learning for Fine-Grained Industrial Defect Severity Grading
- 将严重度分级建模为实例级序数学习，融合显式形态特征
- 引入类别自适应序数阈值，提升对缺陷特异性边界建模能力
- 通过定位扰动实现预测感知训练，增强对噪声预测实例的鲁棒性
细粒度缺陷严重度分级在工业检测中至关重要，但因严重度标签具有序数特性、依赖形态线索，且两阶段流水线中存在清洁标注样本与噪声预测样本之间的训练-测试差异，仍具挑战。本文提出形态感知序数学习（MAOL）框架，将严重度分级建模为实例级序数学习任务，引入显式形态特征增强表示学习，设计类别条件自适应序数阈值以建模缺陷特异性分级边界，并通过定位扰动实现预测感知训练，提升对不完美预测实例的鲁棒性。在清洁区域与预测实例设置下均进行大量实验，结果表明MAOL显著优于基于规则的方法、名义分类模型及现有序数基线，尤其在预测实例场景中表现突出。该方法在2026年高精度制造细粒度严重度分级挑战赛中位列第三。
原文摘要 · Abstract (English)
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 class-conditional adaptive ordinal thresholds to model defect-specific grading boundaries, and employs prediction-aware training via localization perturbation to improve robustness to imperfect predicted instances. Extensive experiments under both clean-ROI and predicted-instance settings demonstrate that MAOL consistently outperforms rule-based methods, nominal classification models, and existing ordinal baselines, especially in the predicted-instance setting. The proposed approach ranked third in the IDA 2026 Challenge on Fine-Grained Severity Grading for High-Precision Manufacturing.
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