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.
By Zhaoyang Wang, Haiyong Chen, Binyi Su, Kun Liu, Kun Wang, Xianen Zhou, Atik Shahariar
arXiv:2606. 08908v1 Announce Type: cross Abstract: Semiconductor lithography inspection requires reliable detection of small pattern defects such as bridge, burr, pinch, and contamination.
By Pangyun Jeong, Jiyeong Kong, Yuehua Hu, Dohee Jeong, Kyung-Tae Kang
The paper introduces the Aero-engine Blade Defect Detector (ABDD), an online adaptive detection framework that uses test-time adaptation to handle domain shifts in aero-engine blade inspection. ABDD employs a Dual-Alignment Strategy combining feature-statistics alignment with pseudo-box alignment to adapt both global visual style and local defect morphology, and incorporates an Uncertainty-aware Box Filtering mechanism to mitigate errors from noisy pseudo labels. A lightweight Sparse Dilated Mona module enables efficient parameter tuning while preventing source-domain forgetting, and the method is validated on CD-AeBD and HD-AeBD datasets, showing improved robustness and practical applicability on an industrial inspection platform.
By Zhaoyang Wang, Haiyong Chen, Dongying Li, Yining Wang, Huapeng Wu, Xinwei Lv, Atik Shahariar
The paper introduces an automated defect detection system for printed circuit board (PCB) pin insertion, combining U‑Net semantic segmentation with contour‑based feature extraction and logistic regression for board‑level pass/fail classification. Segmentation masks generate contour representations of individual pins, from which features such as average contour size are extracted to train the classifier. Evaluated on an industrial dataset and a public PCB pin‑inspection dataset, the method achieved ROC‑AUC scores of 0.990 and 1.000, outperforming PatchCore and instance‑segmentation baselines.
By Nils Rabeneck, Andr\'e Kiunke, Nicole Hoess, Wolfgang Mauerer
The paper introduces FuDU, a streaming active learning framework that enhances real‑time industrial defect detection by quantifying uncertainty at both image and box levels. It employs a Prototype-based Global Uncertainty Quantification module to assess image‑level uncertainty and a Dual‑entropy defect Uncertainty Evaluator for box‑level uncertainty. By fusing these uncertainties through fuzzy inference, FuDU enables expert‑knowledge‑driven adaptive sampling, improving reliability in tasks such as nuclear fuel rod defect detection.
By Zhaoyang Wang, Haiyong Chen, Binyi Su, Xinwei Lyu
arXiv:2608.21967v1 Announce Type: new
Abstract: Automated visual inspection in manufacturing aims to replace slow and inconsistent manual checks, but its economic value depends on whether its decisio...
By Panagiotis Sapoutzoglou, Jessy Ribaira, Martin Kanounnikoff, Bas Tijsma, Christian Gei{\ss}, Maria Pateraki
SePArate is a weakly supervised method for segmenting patterns from defects in semiconductor wafers. It uses only image‑level annotations and follows a three‑phase training process: encoder pretraining, knowledge transfer for spatial cues, and training on synthetic mixed‑defect data. Experiments show that SePArate outperforms baseline approaches in pixel‑level defect segmentation.
By Dain Kwon, Changmin Shin, Sunjong Park, Kanghyun Choi, Hyeyoon Lee, Jaewon Jang, Minseok Choi, Jinho Lee
arXiv:2607. 06637v1 Announce Type: new Abstract: In this work, we propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers.
By Evgenii Kuriabov, David Miller, Jia Li
arXiv:2609.37360v1 Announce Type: new
Abstract: Defect detection systems for industrial condition monitoring can only be relied upon if they are validated, yet defective samples are rare and, for a s...
By Daniel Pr\"oll, Thomas Kraxner, Tobias Schaefer, Sebastian Hegenbart
The paper introduces ISP-AD, the largest publicly available industrial anomaly detection dataset, featuring both synthetic and real defects from a factory floor. It focuses on challenging, small, weakly contrasted surface defects within highly variable structured patterns, addressing the bias of existing datasets toward ideal imaging conditions. Experiments demonstrate that even a small amount of weakly labeled real defects improves model generalization and that synthetic defects can serve as a useful cold‑start baseline for scalable training.
By Paul J. Krassnig, Dieter P. Gruber
arXiv:2608. 07770v1 Announce Type: new Abstract: Automated anomaly detection methods often report strong performance on curated academic benchmarks, but their behavior under real-world industrial conditions is less clear.
By Mike Szklarzewski, CJ George, Gavin Smithson, Christopher Stokes, Dakota Fulp, William M. Jones, Benjamin Wynn, Alexander Ur, Agit Yesiloz, Clint Kallenbach, Mark Swartz, Nathan DeBardeleben, Sharmistha Chakrabarti
arXiv:2509. 22267v5 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery.
By Jo\~ao Paulo Vieira, Victor Afonso Bauler, Rodrigo Kobashikawa Rosa, Danilo Silva