The paper introduces YOLOEZ, a no-code, GUI-based tool that streamlines the entire YOLO model workflow—data labeling, training, and inference—for automated structural defect detection. It demonstrates that YOLOEZ outperforms traditional image‑processing methods across most detection metrics while simplifying deployment for users without programming expertise. The tool aims to lower technical barriers in structural health monitoring, enabling broader adoption of AI-driven inspection for predictive maintenance and intelligent structural systems.
By Michael Holm, Tanner McElroy, Xinghang Zhang, Guang Lin
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:2606. 01023v1 Announce Type: cross Abstract: Visual inspection remains the dominant quality-control practice in woven and tufted carpet production, yet it is slow, subjective, and inconsistent at the line speeds and widths of modern looms.
By Akbar Erkinov
arXiv:2610.08822v1 Announce Type: new
Abstract: Ageing bridge infrastructure is a growing global concern, yet conventional Structural Health Monitoring (SHM) systems are costly and difficult to scale...
By Zihao Liu, Daigo Kawabe, Jiaji Wang, Chul-Woo Kim, Mehrisadat Makki Alamdari
The paper introduces a region‑aware dual‑model inspection framework that uses instance segmentation for seat‑region defects and image‑level classification for shaft‑region anomalies, followed by a deterministic decision fusion layer that applies industrial SOPs to produce a final component‑level decision. In experiments on 380 automotive fuel injector needles, the fusion layer maintained 100% recall of non‑conforming parts while reducing the false‑rejection rate from 57.6% to 10.9%, an 81.1% drop in false positives. The study shows that formalizing acceptance criteria can translate sensitive visual detections into practical, traceable decisions for precision component inspection.
By Srivishnu Ethirajulu Krishnaraj, Rishikesh R. Chopade
Automated optical inspection of precision components with tiny surface defects remains challenging when different regions of the same component exhibit different defect characteristics and, therefore,...
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
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. 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.
By Yicheng Kang, Yuling Jiao, Xin Geng, Mahesh Nagarajan
arXiv:2604. 26633v2 Announce Type: replace-cross Abstract: Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, and collecting balanced training sets is slow and costly.
By Paul Julius K\"uhn, Mika Pommeranz, Arjan Kuijper, Saptarshi Neil Sinha
arXiv:2606. 06375v1 Announce Type: new Abstract: Digital twins (DTs) allow the digitalization of road infrastructure inspection, though this is hindered by limited annotated data.
By Ching Yau Fergus Mok, Lavindra de Silva, Varun Kumar Reja, Ioannis Brilakis
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 syst...