基于视觉的触觉传感实现小型工业部件的异常检测

中文摘要 AI 辅助

小型工业部件（包括亚厘米级零件，其缺陷由几何因素驱动，且标准光学相机难以清晰分辨）的自动检测，需要能直接捕捉精细表面几何形状的传感模态。基于视觉的触觉传感器通过将接触印记转换为与现有深度学习 pipeline 兼容的高分辨率类图像数据，满足了这一需求，但将其有效用于工业异常检测（AD）的研究仍基本未被探索。本研究系统评估了无监督 AD 方法在真实触觉数据集上的表现，该数据集涵盖协作机器人搭载 GelSight Mini 传感器采集的五种真实工业部件。研究在三种受接触式传感部署约束启发的验证场景下，对比了四种特征嵌入方法：SPADE、PaDiM、FAPM 和 InReaCh。具体验证场景包括：Good Fraction 分析，确定稳定性能所需的标称接触的最小数量，该数量直接受凝胶磨损限制，因为每次采集都会使软界面退化；跨位置评估，评估对观察到相同重复表面图案的不同接触位置的泛化能力；低分辨率与高分辨率对比，评估更高分辨率触觉采集的成本效益。总体而言，这项系统基准研究为采用基于视觉的触觉传感进行工业 AD 的研究人员和从业者提供了实用指导，并表明该模态可作为工业质量控制任务的可行替代方案。

英文摘要

Automated inspection of small industrial components, including sub-centimetre-scale parts where defects are geometry-driven and poorly resolved by standard optical cameras, calls for sensing modalities that can directly capture fine surface geometry. Vision-based tactile sensors address this need by converting contact imprints into high-resolution image-like data compatible with existing deep-learning pipelines, yet their effective use for industrial anomaly detection (AD) remains largely unexplored. This work systematically evaluates unsupervised AD methods on a real tactile dataset covering five genuine industrial components acquired with a GelSight Mini sensor mounted on a collaborative robot. Four feature-embedding methods, SPADE, PaDiM, FAPM, and InReaCh, are compared under three validations explicitly motivated by the deployment constraints of contact-based sensing: a Good Fraction analysis establishing the minimum number of nominal contacts for stable performance, directly bounded by gel wear since every acquisition degrades the soft interface; a cross-position evaluation assessing generalization across different contact locations observing the same recurring surface pattern; and a low- versus high-resolution comparison evaluating the cost-benefit of higher-resolution tactile acquisition. Overall, this systematic benchmarking study provides practical guidance for researchers and practitioners adopting vision-based tactile sensing for industrial AD and shows how this modality can serve as a viable alternative for industrial quality-control tasks.

发表机构

• Politecnico di Milano（米兰理工大学）

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