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Machine Learning’s Future in Safety: The AI for Construction Safety Demo

With OHSA reporting that 1 in 10 workers are injured on-site every year, safety is a major priority in the construction industry. We’re thrilled to have partnered with Engineering News-Record (ENR) for the first-ever Artificial Intelligence for Construction Safety Demonstration to explore the potential AI has to help risk monitoring.

Vinnie has flagged for missing safety vestsVinnie has flagged potential for missing safety vests. Image courtesy of ENR.

Millions of photos are generated each week on construction projects all over the world.  What if a computer, trained via machine learning, could “look at” each image and flag the ones depicting workers at safety risk? The goal is not to replace human safety experts, but rather to automate finding scenes showing potential risks and flag them for review, reducing the volume of images that need to be manually checked.  Think of it as “computer-assisted” safety.  

The ENR annual Year in Construction photo contest receives thousands of submissions, all of which are reviewed by safety personnel looking for a few common categories of infractions. In the spirit of other ‘man vs. machine’ events like IBM’s Deep Blue vs. Kasparov, our AI engine Vinnie, for The Very Intelligent Neural Network for Insight & Evaluation, reviewed 2016 contest submissions, alongside the usual human experts.

Why non-tech comanies are beginning to use AI at scale >

Just like people, machine learning engines are highly trainable, and practice makes perfect. To gear up, we leveraged 10 years of data from ENR, as well as site photos from our customer partners - Suffolk, Skanska USA Building, Rogers-O’Brien, Mortenson & Co, and more - to train Vinnie on “seeing” worksite safety hazards. We started small by identifying photos containing workers, then worked on recognizing workers who were missing hard hats, not wearing safety colors, or both.

The results were exciting: Vinnie’s identification took just a few minutes for 1,080 images, and reduced the pool marked for review by over half.  The human team required over 5 hours to do the same.  As the first public demonstration of AI for safety, the demonstration results show how automated tools like Vinnie can help make safety observation faster and more effective. This is just the beginning.

We’d like to thank ENR and our customer partners for all of their help and support in providing data to train Vinnie. Check out our case study highlighting the demonstration,  or, sign up for a free demo to start screening your jobsite and other photos for people, hard hats, and safety vests in the live app. Please share ideas for more things you’d like Vinnie to be able to find, and we’ll work on them!


Written by Josh Kanner

Josh Kanner has been involved in enterprise-focused software startups since 2000 with a focus in the AEC (architecture, engineering and construction) industry since 2005.

Most recently he was co-founder of Vela Systems, a pioneer in the use of web and tablet workflows for construction and capital projects. There he led the company’s product, marketing, and business development functions. Vela Systems grew from bootstrapped beginnings to include over 50% of the ENR Top Contractors as customers and deployments all over the globe. The company was successfully acquired by Autodesk in 2012 and has been rebranded as BIM 360 Field.

Prior to founding Vela Systems, Josh was responsible for product management and strategy at Emptoris (now part of IBM), a web-based strategic sourcing software company with customers including Motorola, GlaxoSmithKline, Bank of America, and American Express.

Kanner graduated from Brown University and earned an MBA from MIT’s Sloan School of Management. He still gets excited to put on a hard hat and walk a job.

View more posts by Josh Kanner.

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