Computer Vision for Construction Safety
AI systems that analyze live and recorded video feeds from construction sites to automatically detect PPE non-compliance, fall hazards, and unsafe behaviors in real time.
Definition
Computer vision safety systems apply deep learning models to construction site camera feeds—CCTV, body-worn cameras, drone imagery, and 360-degree captures—to identify safety hazards and PPE violations automatically. The scale of the problem: approximately 4,800 construction workers die daily worldwide, and 70% of fall fatalities involve lack or improper use of fall protection equipment. Research published in 2025-2026 shows significant technical progress: YOLOv10 with Swin Transformer achieved 92.4% AP50 for non-helmet detection; systems using contextual transformer networks process datasets of 55,000+ images across 28 safety categories; regulation-aligned frameworks assess PPE compliance using visual relationships and scene graph reasoning—going beyond individual item detection to evaluate whether equipment is correctly donned for the specific work-at-height task. Modern implementations extend to: scaffold integrity monitoring, exclusion zone enforcement around heavy equipment, housekeeping assessment, and behavioral analysis. Oracle's Construction Safety AI combines computer vision observations with schedule and payroll data for predictive safety forecasting.
Examples
A camera network on a high-rise automatically detecting workers without harnesses near leading edges and alerting the safety manager
YOLOv10 model processing 72 hours of site camera footage overnight to produce a non-compliance heat map by location
Computer vision flagging that a subcontractor's crew consistently skips hard hat requirements during morning concrete pours
Nomic Use Cases
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Frequently Asked Questions
Computer vision safety systems apply deep learning models to construction site camera feeds—CCTV, body-worn cameras, drone imagery, and 360-degree captures—to identify safety hazards and PPE violations automatically. The scale of the problem: approximately 4,800 construction workers die daily worldwide, and 70% of fall fatalities involve lack or improper use of fall protection equipment. Research published in 2025-2026 shows significant technical progress: YOLOv10 with Swin Transformer achieved 92.4% AP50 for non-helmet detection; systems using contextual transformer networks process datasets of 55,000+ images across 28 safety categories; regulation-aligned frameworks assess PPE compliance using visual relationships and scene graph reasoning—going beyond individual item detection to evaluate whether equipment is correctly donned for the specific work-at-height task. Modern implementations extend to: scaffold integrity monitoring, exclusion zone enforcement around heavy equipment, housekeeping assessment, and behavioral analysis. Oracle's Construction Safety AI combines computer vision observations with schedule and payroll data for predictive safety forecasting.
A camera network on a high-rise automatically detecting workers without harnesses near leading edges and alerting the safety manager. YOLOv10 model processing 72 hours of site camera footage overnight to produce a non-compliance heat map by location. Computer vision flagging that a subcontractor's crew consistently skips hard hat requirements during morning concrete pours.
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