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

2026-09-02

AI Summary

本文提出MAOL框架，通过形态感知和序数学习方法解决工业缺陷精细分级问题，提高对不完美预测实例的鲁棒性。

Abstract

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.

Problem

• Fine-grained Defect Severity Grading
• Ordinal Learning
• Morphology-Related Cues
• Train-Test Discrepancy

Innovation

• Morphology-Aware
• Ordinal Learning
• Class-Conditional Adaptive Ordinal Thresholds
• Prediction-Aware Training