一个统一的视觉语言模型,用于手套制造中的跨产品缺陷检测
Yusen Zhao1,2, Liang Tian3,4, Yonggang Wang3,4
1Institute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang, Hebei, China.
PloS one
|February 11, 2026
概括
一个新的多式大型语言模型 (MLLM) 为工业质量控制提供了统一的缺陷检测. 这种可扩展的框架取代了多种专业模型,提高了制造检查的效率和降低了成本.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 工业质量控制依赖于自动异常检测.
- 目前的深度学习模型面临着可扩展性问题,原因是"一个模型-每个任务"的方法,增加成本和复杂性.
- 需要一个统一的框架来有效地处理不同的制造环境和产品线.
研究的目的:
- 提出使用多式大型语言模型 (MLLM) 的统一缺陷检测框架.
- 解决工业质量控制中传统的单任务深度学习模型的局限性.
- 为了证明单个MLLM用于多产品缺陷检测的可扩展性和成本效益.
主要方法:
- 开发了一个统一的缺陷检测框架,基于多式大型语言模型 (MLLM).
- 采用了两阶段的微调策略:用于域知识的监督微调 (SFT) 和用于视觉推理的强化微调 (RFT).
- 利用多方面的可验证奖励函数来优化RFT期间的本地化,分类和输出结构.
主要成果:
- 在手套制造数据集上,RFT增强的MLLM实现了0.63的平均平均精度 (mAP),与专门的YOLO基线 (0.62) 相比.
- 在混合产品数据上训练的单一统一的MLLM保持了竞争性表现 (mAP 0.61),通过自然语言提示处理不同的产品和缺陷.
- 证明了MLLM能够动态适应各种产品和缺陷类型的能力.
结论:
- 一个单一的,灵活的MLLM可以有效地取代复杂的工业检查中的多个刚性,专业化的模型.
- 拟议的MLLM框架为智能质量控制系统提供了可扩展和具有成本效益的解决方案.
- 这种方法为更具适应性和效率的自动化制造检查铺平了道路.
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