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VLMs benchmarked for textile sorting, Qwen leads accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a digital twin-driven robotic system for automated textile sorting, integrating visual language models (VLMs) for classification and foreign object detection. The system was benchmarked using nine VLMs across various garment types and foreign objects, with the Qwen model family achieving the highest accuracy at 87.9%. Lighter models like Gemma3 were noted for their competitive speed-accuracy trade-offs for edge deployment. This approach combines VLM reasoning with grasp detection and digital twin technology to enhance manipulation reliability and enable scalable, autonomous textile sorting in industrial settings. AI

IMPACT VLMs show promise for industrial automation tasks like textile sorting, with specific models offering different trade-offs for accuracy and deployment.

RANK_REASON The cluster is based on an academic paper detailing a new research methodology and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

VLMs benchmarked for textile sorting, Qwen leads accuracy

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The cluster is based on an academic paper detailing a new research methodology and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Serkan Ergun, Tobias Mitterer, Hubert Zangl ·

    Digital Twin Driven Textile Classification and Foreign Object Recognition in Automated Sorting Systems

    arXiv:2603.05230v2 Announce Type: replace Abstract: The increasing demand for sustainable textile recycling requires robust automation solutions capable of handling deformable garments and detecting foreign objects in cluttered environments. This work presents a digital twin driv…

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