Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines
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. To address these challenges, we introduce a Large-Scale Multi-Modal
The increasing maturity of Large-scale Visual-Language Models (LVLMs) is naturally leading researchers to explore their application in specialized domains like industrial defect detection, where current methodologies are insufficient.
This study introduces necessary benchmarks and baselines for applying advanced AI to critical industrial tasks, paving the way for improved efficiency, quality control, and automation in manufacturing sectors.
The availability of large-scale industrial datasets and refined interaction methods will enable more effective and less subjective deployment of AI in industrial visual inspection, overcoming existing barriers.
- · Industrial automation sector
- · AI/ML researchers in computer vision
- · Manufacturing companies
- · Large-scale Visual-Language Model developers
- · Traditional manual inspection methods
- · Companies reliant on bespoke, inflexible defect detection systems
Improved accuracy and speed in industrial defect detection will reduce waste and increase production efficiency.
The development of robust and adaptable LVLMs for industrial use could accelerate lights-out manufacturing and autonomous factories.
Enhanced industrial quality control through AI could lead to more durable goods and potentially alter supply chain dynamics.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI