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This paper introduces MAOL, a Morphology-Aware Ordinal Learning framework designed to tackle the challenges of fine-grained defect severity grading in industrial inspection. By treating severity grading as an instance-level ordinal learning task and integrating morphological features, MAOL enhances representation learning and utilizes class-conditional adaptive ordinal thresholds to define defect-specific grading boundaries. Extensive experiments reveal that MAOL significantly outperforms traditional methods and existing ordinal models, particularly in scenarios involving noisy predicted instances, achieving notable recognition in the IDA 2026 Challenge.
MAOL outperforms existing methods in fine-grained defect grading by effectively integrating morphological cues and adaptive thresholds, achieving robust performance even with noisy data.
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.
