Intelligent Ppe Detection And Compliance Assurance At Construction Sites Using Improved Yolo Algorithm

• Author(s):

  Lavanya E | Menaka R | Deepika S | Aruna Devi R | Mrs.M.Agalya

• Keywords:

  Construction Safety, Deep Learning, PPE Compliance Detection, Real-time Monitoring, SMS Alert System, Workplace Safety, YOLOv11

• Abstract:

  Construction Sites Are Hazardous Environments For Anyone Working Within Them With Various Dangers Present Due To The Presence Of Heavy Machinery, Unsafe Working Practices, And Inadequate Safety Measures. Personal Protective Equipment (PPE) Like Hardhats, Safety Vests, Gloves, Boots, And Masks Can Be Used To Minimize Injuries And Accidents. It May Be Challenging Manually To Monitor The Compliance Of Workers Regarding Their Adherence To Wearing PPE Since There Are Many Individuals At A Construction Site, And Supervision May Not Be Feasible. The Aim Of This Project Is To Design An Intelligent PPE Detection System Using An Enhanced YOLOv11 Deep Learning Model To Analyze Real-time Data From Cameras At A Construction Site To Verify If Workers Are Wearing Appropriate PPE. The Intelligent PPE Detection System Will Detect Various PPEs Worn By Individual Workers In Real-time And Classify Workers Based On Whether They Comply With PPE Usage Safety Guidelines Or Not. In Case Of Non-compliance By Any Individual, The System Will Automatically Alert The Respective Supervisor Through SMS And Notifications. This Particular System Has Been Created In Such A Way That It Will Work Under Difficult Circumstances That Are Usually Found On Construction Sites, Including Poor Lighting, Crowded Environments, And Partially Blocked Lines Of Sight. Through This Particular System, Continuous And Automatic Monitoring Of Compliance Regarding PPE Will Be Made Possible, Which Will Lead To Improved Safety In The Workplace, Ensuring Regulatory Compliance, Minimizing Workplace Accidents, And Improving Safety On The Construction Site.

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Other Details

• Paper id:

  IJSARTV12I5105322

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-11

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