The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
arXiv:2608. 13937v1 Announce Type: cross Abstract: Smart manufacturing processes are often installed with a large number of sensors, imaging devices and computers, which not only enable instant communication across various modules of a production system but also aid in intelligent manufacturing management.
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.
arXiv:2606. 20323v1 Announce Type: new Abstract: Deep Transfer Learning (DTL) allows for the efficient building of Intelligent Fault Diagnosis Systems (IFDS).
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...
arXiv:2610.15032v1 Announce Type: cross Abstract: Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in whi...
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.