๐ฆ๐ผ๐บ๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐บ๐ผ๐๐ ๐ถ๐บ๐ฝ๐ผ๐ฟ๐๐ฎ๐ป๐ ๐๐ฎ๐ณ๐ฒ๐๐ ๐๐ถ๐ด๐ป๐ฎ๐น๐ ๐บ๐ฎ๐ ๐ป๐ฒ๐๐ฒ๐ฟ ๐บ๐ฎ๐ธ๐ฒ ๐ถ๐ ๐ถ๐ป๐๐ผ ๐ฎ๐ป ๐๐๐ฆ ๐๐๐๐๐ฒ๐บ. ๐๐ผ๐บ๐ฝ๐๐๐ฒ๐ฟ ๐ฉ๐ถ๐๐ถ๐ผ๐ป ๐๐ ๐ฐ๐ฎ๐ป ๐ต๐ฒ๐น๐ฝ ๐๐ฒ๐ฎ๐บ๐ ๐๐ฒ๐ฒ ๐บ๐ผ๐ฟ๐ฒ ๐ผ๐ณ ๐๐ต๐ฎ๐ ๐ถ๐ ๐ต๐ฎ๐ฝ๐ฝ๐ฒ๐ป๐ถ๐ป๐ด ๐ฏ๐ฒ๐ณ๐ผ๐ฟ๐ฒ ๐ฎ ๐ฟ๐ถ๐๐ธ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ๐ ๐ฎ๐ป ๐ถ๐ป๐ฐ๐ถ๐ฑ๐ฒ๐ป๐.
At the 2026 Europe IMPACT Conference, the Automated Near Miss Reporting with Computer Vision AI session explored how existing CCTV infrastructure can support more proactive safety visibility.
Subhash Sharma, Founder & CEO of Hawkvision AI, shared how Computer Vision AI can help identify defined safety events such as PPE non-compliance, unsafe pedestrian and vehicle interactions, walkway issues, spills, and potential near misses that may otherwise go unreported.
When those signals are connected with Benchmark Gensuite Concern Reporting, they can move directly into established EHS workflows for review, investigation, action, and follow-up.
The discussion highlighted several practical considerations:
โข Use existing camera infrastructure to identify previously unseen safety signals
โข Focus Computer Vision AI on defined, site-specific risks and use cases
โข Connect AI-detected events directly into structured EHS workflows
โข Prioritise the signals that require action rather than creating more alerts
โข Build privacy, transparency, and clear safety purpose into deployment from the start
The opportunity is not simply to detect more events. It is to turn relevant safety signals into information EHS teams can assess, prioritise, and act on.
Read the full blog to explore how Computer Vision AI can strengthen near miss reporting and proactive risk visibility:
https://hubs.la/Q04tG5jJ0
EHSManagement WorkplaceSafety ArtificialIntelligence ComputerVision
