Safe, Secure and accountable construction, driven by AI computer vision
AI has the ability to transform construction, with more efficient projects, executed faster and safer than ever before. Using computer vision to check for health and safety compliance (PPE , machine-human safe distancing, ...), material usage, security helps drive effective projects. Real sites are hazardous and dynamic, with changing lighting, weather, geolocation, and camera views including drones. Capturing rare incidents at scale is slow and costly. Synthetic datasets for CV can provide relevant data to train automated vision systems giving robust and reliable performance in real deployment conditions.
Why Traditional Approaches Fall Short
Real world capture disrupts work and misses rare hazards. Drone and perimeter views change constantly and annotations are expensive. Lighting, shadows, and weather shift by location and time, causing brittle models. Road damage varies by type and texture, which is hard to sample broadly. These limits hinder depth prediction with synthetic data and normal estimation synthetic performance in production.
How Synthera™ Solves It
Synthera™ uses real world modelled simulation for data generation to create effective data that models the real world environments. Synthera's Digital Humans infinitely vary skin tone, clothing, and equipment to cretae a fully diverse dataset. Modeling the real world continues with synthetic generation of cracks, potholes, mosaics, and geolocated sun and weather. Flexible camera types including synchronized views of multi-cam. Outputs support multi modal AI with synthetics for computer vision synthetic training at scale.
Use Case Examples
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PPE compliance and violation detection
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Hazard zone intrusion monitoring
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Worker proximity to heavy equipment
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Material and tool tracking analytics
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Road crack and pothole detection
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Weather and shadow robustness testing
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Drone and perimeter camera optimization
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Background segmentation synthetic training
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Depth and normal ground truth generation
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Video surveillance synthetic data for sites
