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:2606. 07965v1 Announce Type: new Abstract: Large Visual Language Models (LVLMs) have achieved remarkable success in vision tasks.
arXiv:2607. 10666v1 Announce Type: cross Abstract: Deploying AI-based visual inspection in manufacturing is hard because requirements change often, new defect types appear, and large labeled datasets are rarely available.
arXiv:2608.29783v1 Announce Type: new Abstract: Industrial anomaly detection is a critical component of modern manufacturing. Most traditional unsupervised methods rely on modelling normal feature di...
arXiv:2606. 07953v1 Announce Type: new Abstract: Large-scale Visual-Language Models (LVLMs) have achieved remarkable success in natural visual tasks, yet their application to industrial defect detection remains challenging due to two fundamental limitations: (i) the scarcity of large-scale industrial datasets that cover diverse defect categories across multiple domains, and (ii) the reliance on manual prompts (points, boxes, masks) that introduce subjective noise and lack text-visual interaction for fine-grained understanding.
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.
arXiv:2607. 10826v1 Announce Type: cross Abstract: Automated evaluation is essential for scaling generative 3D systems, where exhaustive human review is costly and slow.
arXiv:2607. 12245v1 Announce Type: cross Abstract: Few-shot industrial defect detection remains difficult for standard supervised detectors, which achieve poor performance on boundary-dominated industrial defects.
SalArt-VQA is a diagnostic benchmark that tests whether vision‑language models can understand salient artifacts in AI‑generated images. It includes 950 images and 3,681 multiple‑choice questions that assess artifact presence, semantic localization, spatial grounding, and evidence‑grounded defect identification. The benchmark reveals that high image‑level detection accuracy can mask failures in grounded understanding, showing a trade‑off between sensitivity and calibration.
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.
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,...
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.