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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

classification cs.CVcs.LG
keywords datasetsvisionindustrialinspectiondefectvision-basedannotationchallenges
verification ladder T0 review T1 audit T2 compute T3 formal
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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 3 Pith papers

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

  1. EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A difficulty-aware GRPO training scheme with response resampling, advantage reweighting, GPT-generated text samples, and heatmap-guided contrastive embeddings improves InternVL3-8B by 7.77 percentage points on the MMA...

  2. Analyze-Prompt-Reason: A Collaborative Agent-Based Framework for Multi-Image Vision-Language Reasoning

    cs.CV 2025-08 conditional novelty 3.0 of 10

    A prompt-engineered Claude 3.7, guided by GPT-4o-generated prompts and few-shot examples, reaches near-ceiling accuracy on most of the 18 MIRAGE multi-image reasoning tasks.

  3. A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects

    cs.CV 2025-07 conditional novelty 3.0 of 10

    A broad survey of industrial defect detection that structures the field by closed-set vs open-set and 2D vs 3D methods, with an emphasis on the rise of open-set anomaly detection.

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