Computer Vision is driving the future of efficient, safe transportation
Synthera™ synthetic data gets you there faster
Transportation infrastructure benefits from AI powered computer vision, if those models are accurate, and have been trained on adequately diverse datasets. They enhance tasks such as across varied environments. such as rail, road, shipping and air, ensuring efficient safe systems.
Real world data gathering is expensive, and rarely captures rare or unsafe events. Synthetic datasets for CV, generated through simulation based data generation, provide scalable, photorealistic training that improves generalization for transportation computer vision applications
Why Traditional Approaches Fall Short
Real world capture cannot scale to the diversity and volume needed for robust AI training. Capturing rare events, such as abandoned baggage, weapons carrying, crowded platforms is slow, costly, and incomplete. Traditional datasets also lack control over diversity of human subjects in all physical aspects, leading to unintended bias.
How Synthera™ Solves It
Synthera™ creates transportation specific photorealistic synthetic images with full control over lighting, weather, vehicles, and actors. Using advanced simulation engines, we replicate diverse environments, vehicle types, and behavioural scenarios at scale. Our datasets include rich annotations such as 2D and 3D bounding boxes, semantic segmentation, and motion metadata, enabling AI systems to excel in real world conditions for autonomous driving, monitoring, and safety systems.
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Use Case Examples
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Abandoned bag detection
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Fare Evasion
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Anti-social behaviour detection
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Dangerous activity detection
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obstruction detection
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Overcrowding/Passenger flow management
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Weapon detection
