Anomaly Detection Datasets for Industrial and Security AI
Normal and anomalous sample pairs with precise defect annotations, rare anomaly coverage, and domain-specific defect taxonomies. Built for production visual anomaly detection in manufacturing, security, and infrastructure monitoring.
20+
Years in AI training data
1,000+
Language locales
1M+
Hours of video annotated
ISO
27001 certified
The Challenge
Beyond General Anomaly Benchmarks
MVTec AD and VisA established surface defect detection benchmarks. Production industrial anomaly detection requires defect taxonomies, imaging configurations, and normal sample distributions specific to your product and process.
Electronics PCB inspection, textile surface defect detection, and food quality monitoring each require normal and anomalous samples from your exact production line, under your imaging conditions. Generic benchmark surface textures cannot provide this.
Off-the-shelf anomaly datasets suffer from product mismatch (benchmark surfaces differ from your components) and defect taxonomy gaps: MVTec's 15 object categories share none of your production defect classes.
LXT builds custom anomaly detection datasets from your production line or domain. We collect normal samples in quantity and engineer controlled defect samples or identify real defects in production footage, with precise anomaly annotations and defect taxonomy documentation.
Why Teams Upgrade
Limitations of Public Anomaly Detection Datasets
Standard benchmarks serve research well. Production deployments need more.
| Dataset | Primary Limitation | Impact |
|---|---|---|
| MVTec AD | 15 texture and object categories unrelated to most production lines; controlled lab imaging; no domain-specific defect types | Lab textures |
| BTAD | 3 industrial products only; limited defect type coverage; specific imaging conditions not replicable for other production lines | 3 products |
| VisA | 12 objects; still limited to benchmark categories; PCB and electronic objects only; no textile, food, or custom industrial types | 12 objects |
| MPDD | Metal part surface defects only; limited to specific metal components; no coverage of other material types | Metal-only |
| MVTec LOCO | Logical anomalies in 5 categories; narrow scope; not applicable to general manufacturing inspection scenarios | 5 categories |
Not sure which specs you need?
Our data specialists help you scope the right dataset for your model architecture.
Configurable Specifications
Specs Built Around Your Model
Public datasets come fixed. Yours is configured for your architecture, environment, and use case.
Sample Collection
Data Protocol
- Normal Samples: High-volume nominal product samples from your production run
- Defect Samples: Real production rejects or controlled defect induction with ground truth
- Imaging Setup: Matched to your production line camera, lighting, and part handling
Defect Taxonomy
Anomaly Classes
- Defect Types: Domain-specific defect classes designed with your QA team
- Severity Levels: Grade A-D severity annotation for defect severity estimation models
- Location Labels: Defect region bounding boxes and pixel masks per anomaly
Dataset Balance
Normal-Anomaly Ratio
- Normal Volume: High-volume normal samples for distribution learning models
- Anomaly Coverage: Minimum instances per defect type with rare class guarantees
- Synthetic Options: Controlled defect induction protocol for rare defect augmentation
Need a custom configuration?
We've built datasets across dozens of domains and use cases. Let's scope yours.
Capturing Complexity
Edge Cases in Anomaly Detection
High-accuracy models handle rare attributes that public datasets miss.
Subtle and Near-Normal Defects
Early-stage defects and surface micro-scratches are difficult to distinguish from normal variation. High-magnification imaging and expert quality inspector annotation identify subtle defect boundaries.
Normal Appearance Variation
Product components vary in appearance within tolerance. Sufficient normal sample volume across production run variation prevents false positive alarms on normal product variation.
Rare Defect Types
Critical defects that cause field failures may appear rarely in production. Controlled defect induction or targeted production run monitoring ensures minimum rare defect instance coverage.
Multi-Object and Assembly Anomalies
Assembled product anomalies involve incorrect component placement or missing parts. Logical anomaly annotation captures structural and relational defects beyond surface texture anomalies.
Ground Truth Quality
Human-in-the-Loop Annotation
Precise annotation bridges raw data and learnable signal. Expert annotators deliver precision automated tools can't match.
Defect Region Annotation
Quality inspection experts annotate defect regions with pixel-precise masks and bounding boxes. Defect type and severity classification per annotation.
Defect Taxonomy Documentation
Complete defect taxonomy with visual reference examples, severity grade definitions, and boundary case documentation for ongoing annotation and model maintenance.
Normal Distribution Analysis
Statistical analysis of normal sample variation range, key feature distributions, and natural variation bounds delivered with the dataset for threshold calibration.
Industry Applications
Anomaly Detection Datasets for Your Domain
Custom taxonomies and collection protocols for specific deployment contexts.
Manufacturing QA
PCB, casting, machined parts, textile inspection
Food Safety
Defect detection, foreign object, freshness inspection
Pharmaceutical
Tablet, capsule, and packaging defect inspection
Infrastructure
Road, bridge, and utility surface inspection
Security
Anomalous behavior and object detection
Electronics
Solder joint, component, and board inspection
Textiles
Fabric defect detection, weave pattern anomalies
Remote Sensing
Environmental change detection, satellite anomaly
Compliance & Ethics
Secure and Ethical Data Collection
Data collection involving people and sensitive content requires robust security, compliance, and ethical protocols at every stage.
Global Demographic Reach
Collection across 1,000+ locales and diverse demographics to prevent algorithmic bias in your deployed models.
ISO 27001 Certified
Sensitive projects processed in certified secure facilities meeting the highest information security standards.
GDPR & Privacy Compliance
All collection and annotation protocols vetted for consent and privacy. Legally robust for global deployment.
Frequently Asked Questions
Anomaly Detection Dataset FAQs
Can you collect from our existing production line?+
Yes. We coordinate on-site or remote collection from your production imaging setup. Sample collection protocols are designed to minimize production disruption.
How do you generate rare defect examples?+
We use controlled defect induction protocols (scribing, contamination, component removal) to produce representative rare defect examples. All induction methods are documented in delivery.
What normal to anomaly ratio do you recommend?+
For unsupervised anomaly detection models, 100:1 to 1000:1 normal-to-anomaly ratios are typical. We advise on ratio targets based on your model architecture and defect prevalence.
Do you provide pixel-level defect masks?+
Yes. Pixel-level defect masks alongside bounding boxes are available. Expert quality inspectors perform annotation with defect type and severity labels.
What does a custom anomaly detection dataset cost?+
Projects range from $15K for focused single-product datasets (5,000-20,000 normal + 500-2,000 anomaly samples) to $100K+ for large multi-product, multi-defect collections.
Can you cover texture and logical anomalies?+
Yes. Surface texture defects are annotated with pixel masks. Logical anomalies (missing components, wrong assembly) are annotated with component-level labels.
How do you handle proprietary product confidentiality?+
All collection operates under strict NDA. Images are stored in access-controlled environments and not used for any purpose outside your project scope.
Get Started
Scope Your Custom Anomaly Detection Dataset
Share your product type, defect taxonomy, and imaging setup. An industrial AI data specialist will provide a detailed proposal within 48 hours.
Please provide us with the details of your inquiry and one of our team members will be in touch.
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