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AI-driven real-time error detection

Construction worker on scaffolding

The Challenge

Currently the process infrastructure builders and operators use to document work done on an asset is quite manual and retrospective, requiring a fitter to use a mobile device take photographs of their daily work on a site, a case handler to review and spot check the reported photos and flag errors such as jobs left incomplete or not done correctly, and a worker to go out to “fix” the potentially hazardous errors.

This challenge, set in conjunction with Infratek, sought innovative proposals for solutions that can harness the capabilities of computer vision, artificial intelligence and machine learning to not only improve how quickly errors are spotted in work on infrastructure assets, but to go further to offer real-time detection and feedback to the worker prior to leaving site to ensure the job is done right the first time, and improve safety for the worker and third parties.

Start-up NumberBoost talks about their journey with the Safety Accelerator

Take a look back at our previous safety and risk challenges.

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