Safety Shield Global deploys Edge AI to cut heavy-industry collisions
Safety Shield Global, a UK-based industrial safety technology company, is positioning computer vision and Edge AI as a practical layer of worker protection for some of the world's most hazardous environments. The company's Human Form Recognition system detects people in the vicinity of heavy mobile plant and delivers real-time visual and audible warnings to operators. According to the company, the technology has achieved a reported 99.6% detection accuracy and has reduced incidents by an average of 70% on the sites where it has been deployed.
The firm has been developing the technology for more than 15 years, with founder and chief executive Jonathan Guest citing a personal motivation: a close friend was killed in an excavator collision on a construction site. What began as an effort to prevent similar tragedies has since grown into a commercial platform used across construction, quarrying, forestry, logistics, agriculture and waste management.
Edge AI as the operational enabler
A central technical argument in Safety Shield's approach is that cloud-dependent AI cannot meet the latency requirements of industrial safety. The company processes data locally on the machine rather than routing it through remote infrastructure, allowing detection systems to operate in milliseconds and to continue functioning where network connectivity is absent or unreliable. That emphasis on edge processing is increasingly shared across the industrial automation sector, where real-time decision-making is a hard constraint rather than a preference.
Simon Bailey, Head of Research and Development at Safety Shield Global, said advances in machine learning and edge hardware have materially changed what is achievable in industrial environments. "Advances in computer vision, machine learning and edge processing have significantly improved detection accuracy while reducing false positives. We're continually evaluating emerging AI models and hardware to ensure our technology evolves alongside the wider AI landscape."
The company has also begun integrating its technology directly into new machinery. A collaboration with Komatsu to introduce a factory-fit Human Detection System marks a shift from aftermarket installation to native integration, with safety intelligence built into the machine from the point of manufacture rather than added on site.
Market context and competitive landscape
Industrial computer vision for worker safety is an active and growing segment of the broader machine-vision market. A range of vendors, from large industrial automation groups to specialist startups, are developing proximity-detection and collision-avoidance systems, and several OEMs are beginning to treat embedded safety AI as a standard specification rather than an optional extra. Regulatory pressure is an additional driver: in the UK, the Health and Safety Executive has long identified struck-by and run-over incidents involving mobile plant as among the most serious causes of fatal workplace accidents, and site operators face increasing scrutiny over how they mitigate those risks.
Safety Shield's King's Award for Enterprise: Innovation, received in 2024, provides a degree of independent validation, though the company's performance claims, the 99.6% detection figure and 70% incident reduction, are self-reported and have not been verified against a named third-party audit. Buyers in regulated sectors would typically seek independent benchmark data before procurement.
The next phase: from detection to risk understanding
Safety Shield is developing its Nexus platform to move beyond identifying the presence of people and towards categorising and contextualising risk. Bailey described the longer-term aim as building AI systems that understand context, predict potential risks and communicate across connected job sites. That trajectory aligns with broader industry ambitions around connected worker platforms, where data from cameras, sensors and telematics is aggregated to give operators and site managers a fuller operational picture.
The company is careful to frame AI as a supplementary layer rather than a replacement for training, human judgement and safety culture, a position that reflects both a practical reality and a likely regulatory expectation as AI governance frameworks extend into physical work environments. The EU AI Act, for instance, classifies certain AI safety components in machinery as high-risk applications, which will carry conformity-assessment requirements as the regulation's provisions phase in.