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VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON

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arxiv 2306.07890 v2 pith:DGXXM3ZB submitted 2023-06-13 cs.CV cs.LG

VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON

classification cs.CV cs.LG
keywords datasetsvisionindustrialinspectiondefectvision-basedannotationchallenges
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often remain under-addressed. We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges. Unlike previous datasets, VISION brings versatility to defect detection, offering annotation masks across all splits and catering to various detection methodologies. Our datasets also feature instance-segmentation annotation, enabling precise defect identification. With a total of 18k images encompassing 44 defect types, VISION strives to mirror a wide range of real-world production scenarios. By supporting two ongoing challenge competitions on the VISION Datasets, we hope to foster further advancements in vision-based industrial inspection.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting

    cs.CV 2026-04 unverdicted novelty 7.0

    UniSpector organizes visual prompt space with spatial-spectral and contrastive encoders to support open-set defect localization, beating baselines by at least 19.7% AP50b and 15.8% AP50m on the new Inspect Anything benchmark.

  2. Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines

    cs.AI 2026-06 unverdicted novelty 6.0

    Presents MMIOC-1M benchmark with 1M+ samples across 14 super-categories and RTVPNet with domain projection, sparse sampling, and bidirectional interaction, claiming SOTA on MMIOC-1M, LVIS, and COCO.