Anomaly Detection on Small Industrial Components via Vision-Based Tactile Sensing

• URL: http://arxiv.org/abs/2608.30506v1
• Date: Mon, 31 Aug 2026 09:40:35 GMT
• Title: Anomaly Detection on Small Industrial Components via Vision-Based Tactile Sensing
• Abstract summary: Vision-based tactile sensors convert contact imprints into high-resolution image-like data compatible with existing deep-learning pipelines.<n>This work systematically evaluates unsupervised anomaly detection methods on a real tactile dataset.<n>It shows how this modality can serve as a viable alternative for industrial quality-control tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Toward Geometry-Scalable Whole-Body Touch for Humanoids: A 3D-Printed Conformal EIT Skin [66.36050076244905]
  Whole-body tactile sensing is a prerequisite for humanoids that operate in contact-rich human environments.<n>We present a conformal electrical tactile tomography skin fabricated through a geometry-adaptable additive-manufacturing workflow.
  arXiv  Detail & Related papers  (2026-08-03T11:33:30Z)
• UniForce: A Unified Latent Force Model for Robot Manipulation with Diverse Tactile Sensors [51.88112610411651]
  We propose a novel unified tactile representation learning framework that learns a shared latent force space across diverse tactile sensors.<n>UniForce reduces cross-sensor domain shift by jointly modeling inverse dynamics (image-to-force) and forward dynamics (force-to-image)<n>To avoid reliance on expensive external force/torque (F/T) sensors, we exploit static equilibrium and collect force-paired data via direct sensor--object--sensor interactions.
  arXiv  Detail & Related papers  (2026-02-01T11:03:01Z)
• Zero-Shot Multi-Criteria Visual Quality Inspection for Semi-Controlled Industrial Environments via Real-Time 3D Digital Twin Simulation [5.0268543063681195]
  We propose a pose-agnostic, zero-shot quality inspection framework that compares real scenes against real-time Digital Twins (DT) in the RGB-D space.<n>Our approach enables efficient real-time DT rendering by semantically describing industrial scenes through object detection and pose estimation.<n>Based on an automotive use case featuring the quality inspection of an axial flux motor, we demonstrate the effectiveness of our framework.
  arXiv  Detail & Related papers  (2025-11-28T14:19:31Z)
• Robust Component Detection for Flexible Manufacturing: A Deep Learning Approach to Tray-Free Object Recognition under Variable Lighting [0.0]
  We implement and evaluate a Mask R-CNN-based approach on a complete pen manufacturing line at ZHAW.<n>Our system achieves 95% detection accuracy across diverse lighting conditions while eliminating the need for structured component placement.
  arXiv  Detail & Related papers  (2025-07-01T15:23:54Z)
• Advances in Compliance Detection: Novel Models Using Vision-Based Tactile Sensors [0.7199733380797579]
  Compliance is a critical parameter for describing objects in engineering, agriculture, and biomedical applications.<n>Traditional compliance detection methods are limited by their lack of portability and scalability, rely on specialized, often expensive equipment, and are unsuitable for robotic applications.<n>We propose two models based on Long-term Recurrent Convolutional Networks (LRCNs) and Transformer architectures that leverage RGB tactile images and other information captured by the vision-based sensor GelSight to predict compliance metrics accurately.
  arXiv  Detail & Related papers  (2025-06-17T21:10:05Z)
• Towards Generalization of Tactile Image Generation: Reference-Free Evaluation in a Leakage-Free Setting [25.355424080824996]
  Tactile sensing is critical for human perception and underpins applications in computer vision, robotics, and multimodal learning.<n>Because tactile data is often scarce and costly to acquire, generating synthetic tactile images provides a scalable solution to augment real-world measurements.<n>We demonstrate that overlapping training and test samples in commonly used datasets inflate performance metrics, obscuring the true generalizability of tactile models.
  arXiv  Detail & Related papers  (2025-03-10T02:37:22Z)
• ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects [0.0]
  TheISP-AD is the largest publicly available industrial dataset to date, including both synthetic and real defects collected directly from the factory floor.<n>Experiments show that even a small amount of injected, weakly labeled real defects improves generalization.
  arXiv  Detail & Related papers  (2025-03-06T21:56:31Z)
• Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties [22.51903015541254]
  MulSen-AD is the first high-resolution, multi-sensor anomaly detection dataset tailored for industrial applications.<n>The dataset spans 15 industrial products with diverse, real-world anomalies.<n>Our experiments demonstrate that multi-sensor fusion substantially outperforms single-sensor approaches.
  arXiv  Detail & Related papers  (2024-12-19T07:23:17Z)
• Elastic Tactile Simulation Towards Tactile-Visual Perception [58.44106915440858]
  We propose Elastic Interaction of Particles (EIP) for tactile simulation. EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact. We further propose a tactile-visual perception network that enables information fusion between tactile data and visual images.
  arXiv  Detail & Related papers  (2021-08-11T03:49:59Z)
• Cognitive Visual Inspection Service for LCD Manufacturing Industry [80.63336968475889]
  This paper discloses a novel visual inspection system for liquid crystal display (LCD), which is currently a dominant type in the FPD industry. System is based on two cornerstones: robust/high-performance defect recognition model and cognitive visual inspection service architecture.
  arXiv  Detail & Related papers  (2021-01-11T08:14:35Z)
• Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition [131.6328804788164]
  We propose a framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) The SAKDN uses multiple wearable-sensors as teacher modalities and uses RGB videos as student modality.
  arXiv  Detail & Related papers  (2020-09-01T03:38:31Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.