[{"data":1,"prerenderedAt":106},["ShallowReactive",2],{"profile":3,"work-industrial-safety-vision":14},{"name":4,"role":5,"tagline":6,"location":7,"contact":8},"Sankar Vema","AI Builder & Architect of Agentic Systems","I help enterprise leaders turn AI ambition into capability that actually ships.","India · open to global advisory engagements",{"email":9,"linkedin":10,"github":11,"blog":12,"twitter":13},"sankar.vema@gmail.com","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fsankarvema\u002F","http:\u002F\u002Fsankarvema.github.io\u002F","http:\u002F\u002Fsankarvema.blogspot.com\u002F","https:\u002F\u002Ftwitter.com\u002Fsansvema",{"id":15,"title":16,"body":17,"context":79,"description":71,"domain":80,"draft":81,"extension":82,"hero":83,"impact":84,"meta":85,"navigation":86,"path":87,"role":88,"scale":89,"seo":90,"slug":91,"stack":92,"status":97,"stem":98,"tags":99,"year":104,"__hash__":105},"work\u002Fwork\u002Findustrial-safety-vision.md","Industrial Safety Vision Analytics",{"type":18,"value":19,"toc":70},"minimark",[20,25,29,33,36,40,63,67],[21,22,24],"h2",{"id":23},"the-mandate","The mandate",[26,27,28],"p",{},"I architected a vision-analytics safety system for an industrial site in the Australian\nmining sector. The goal was blunt: use the cameras already on site to catch the conditions\nthat precede an injury, and raise them while there is still time to act.",[21,30,32],{"id":31},"the-problem","The problem",[26,34,35],{},"Heavy industrial environments are dangerous in ways that are visible but not watched.\nA person in the wrong zone, missing protective equipment, a machine interaction that\nshould not be happening: all of it is on camera, and none of it is seen until after\nsomething goes wrong, because no human can watch every feed continuously. Safety in these\nsettings is a monitoring problem at a scale humans cannot hold.",[21,37,39],{"id":38},"the-system","The system",[41,42,43,51,57],"ul",{},[44,45,46,50],"li",{},[47,48,49],"strong",{},"Real-time video analytics."," Deep-learning models reading live camera feeds to detect\npeople, zones, equipment and unsafe interactions frame by frame.",[44,52,53,56],{},[47,54,55],{},"Protective-equipment and zone checks."," Detection of missing safety gear and presence\nin restricted or hazardous zones, turned into alerts rather than after-the-fact reports.",[44,58,59,62],{},[47,60,61],{},"Edge-aware inference."," Inference placed to meet the latency and connectivity reality\nof an industrial site, so a warning arrives in time to matter.",[21,64,66],{"id":65},"what-it-proves","What it proves",[26,68,69],{},"Industrial vision is where computer vision stops being a benchmark and starts carrying a\nphysical cost for a wrong call. Building a safety system that operators trust means tuning\nfor the failure that hurts a person, not the one that dents an accuracy metric.",{"title":71,"searchDepth":72,"depth":72,"links":73},"",3,[74,76,77,78],{"id":23,"depth":75,"text":24},2,{"id":31,"depth":75,"text":32},{"id":38,"depth":75,"text":39},{"id":65,"depth":75,"text":66},"Australian mining engagement","industrial-vision",false,"md","A computer-vision safety system for the Australian mining industry that reads live site feeds and flags hazards and protective-equipment violations before they become incidents.","Hazards flagged from live camera feeds in real time",{},true,"\u002Fwork\u002Findustrial-safety-vision","Solution Architect","Operational safety, industrial site",{"title":16,"description":71},"industrial-safety-vision",[93,94,95,96],"Computer Vision","Deep Learning","Video Analytics","Edge Inference","shipped","work\u002Findustrial-safety-vision",[100,101,102,103],"build","vision","industrial","safety","2021","uwSCjSkTkJazjmf3qx2texnv412K-A69ybLk4ZqpUlM",1790601541946]