Workplace fatality rates have remained steady over the past several decades despite concerted efforts to reduce serious injury and death on the job. Recognizing this trend, the National Safety Council released a new white paper through titled “Using Computer Vision as a Risk Mitigation Tool.”

The paper outlines the ways artificial-intelligence-paired camera technology and video analytics identify risks across a variety of environments. Further, the white paper compiled case studies and interview data from previously published reports to outline the most promising trends and resources employers can leverage to more effectively prevent worker injury and death.

"Every worker deserves the promise of returning home safely from work at the end of each day, but nationwide, 3.4 fatalities occur per every 100,000 full-time equivalent workers," said Paul Vincent, NSC executive vice president of workplace practice. "While computer vision is being adopted by more organizations, the benefits are not widely understood and used to their full potential.”

Due to its ability to track and log data and instantly deploy information to predict when incidents may occur, computer vision is ideal for industries involving heavy machinery and extensive movement, such as restoration.

Computer vision is especially effective in identifying when personal protective equipment is being properly utilized, such as in the case of employees wearing hard hats and high-visibility vests. This enables managers to more efficiently mitigate unsafe situations

By detecting irregular situations in the workplace, such as unwanted guests, unusual behaviors and the presence of weapons, computer vision can help employers to identify and prevent workplace violence. In addition, this technology can be used to help monitor fatigue and other impairing conditions when driving.  

Computer vision also has applications that can improve workplace health by monitoring ill employees, including with whom they have interacted and objects in which they may have touched. Some computer vision systems can even recognize best practices during emergency situations.

The research team also uncovered limitations with current computer vision technology. These include image quality on closed-circuit televisions and A.I. software's ability to operate in unfamiliar settings. Other common challenges to widespread computer vision adoption, include cost and system security barriers.