Smarter Industrial AI with
Scalable Synthetic Datasets
Industrial environments require computer vision synthetic training for tasks like object counting, defect detection, and PPE compliance. Real factory floors and work sites rarely produce enough defective products or safety violations for robust AI training. Capturing such events disrupts operations, is expensive, and can introduce hazards. Synthetic images and synthetic datasets for CV make it possible to scale data generation safely and accurately.
Why Traditional Approaches Fall Short
Real world data collection is slow, risky, and incomplete. Rare defects, safety breaches, and edge cases are underrepresented, creating biased models. Camera angles, lighting, and backgrounds are often fixed, limiting generalization. Annotation is costly and inconsistent, making it harder to support depth prediction with synthetic data, normal estimation synthetic, and background segmentation synthetic in production AI systems.
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How Synthera™ Solves It
Synthera™ uses simulation based data generation and photorealistic image synthesis to create large scale industrial datasets. We simulate conveyor belts, gravity drop tests, and configurable actors with varied PPE. Procedural tools generate deformations, cracks, rust, and scratches on components. Flexible camera pipelines and synthetic backgrounds enhance robustness. Outputs include two dimensional and three dimensional boxes, semantic segmentation, and rich metadata for computer vision synthetic training.
Use Case Examples
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Object counting on conveyor lines
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Defect detection with procedural damage
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PPE compliance monitoring at scale
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Worker safety distance detection
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Tool and material tracking analytics
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Area incursions
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Scrap material sorting
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Autonomous vehicle operations
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Robotic vehicle/machine vision
