AI applications are transforming industries worldwide, speeding up operations and helping companies streamline end-to-end processes. The technology is also being implemented to improve safety standards, particularly in the construction industry where safety hazards can emerge quickly in complex site settings.
IoT solutions provider, InHand Networks, is among those adopting this approach with its new edge-based construction-site safety management. With on-site video analytics, the EC5000 Series AI Edge Computer helps teams identify risks and trigger alerts faster locally, without the need to rely on cloud video streaming.
Video surveillance is already standard on most large-scale construction sites, but enhanced visibility as of itself does not always prevent accidents. With cloud-only video analytics, one important challenge remains – response delays. When footage needs to be manually reviewed before being escalated to the necessary departments, valuable time is often lost, reducing any chance to intervene before an accident occurs. Moreover, poor connectivity, fluctuating bandwidth, and changing camera angles can also limit cloud videos.
InHand’s latest edge-based safety management system is designed to overcome such obstacles, with video streams available on site, enabling real-time and faster decisions. Local AI analysis supports detection of common safety hazards, including restricted-zone breaches and PPE non-compliance.
The hardware can also initiate actions based on site events, with notifications on mobile workflows and management dashboards. Following incidents, event evidence is also stored and available for review and safety training.
The EC5000 demonstrates how advanced AI can transform construction workflows. Supporting day-to-day operations on construction sites, it promises to reduce the reliance on continuous cloud streaming, ultimately providing faster response times, more resilient operations, and flexible deployment, improving safety and operational efficiency in the industry.
(Image source: “Construction Worker, Amsterdam” by mbrochh is licensed under CC BY-SA 2.0.)
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