Yolo-minesafe: A Vision-based Abnormal Fall Detection And Emergency Alert Framework For Isolated Mining Workers

• Author(s):

  Dr. A. Mary Beula | Kingston J | Kowshik S | Vishal J | Sameer Ahamed S

• Keywords:

  YOLOv8, Fall Detection, Mining Safety, Deep Learning, Computer Vision, Emergency Alert System, Posture Analysis, Real-Time Monitoring, Worker Safety, Intelligent Surveillance.

• Abstract:

  Mining Operations Consistently Rank Among The World’s Most Hazardous Occupational Environments, With Workers Stationed In Isolated Areas Facing Undetected Fall Risks, Sudden Health Emergencies, And Life-threatening Incidents That Current Safety Systems Cannot Address In Real-time. Existing Solutions, Such As Passive Closed-circuit Television (CCTV), Wearable Accelerometers, And Manual Supervision, Fail To Deliver Autonomous, Real-time Incident Detection Across The Expansive And Harsh Terrain Of Active Mine Sites. This Study Introduces YOLO-MineSafe, A Vision-based Fall Detection And Emergency Alert Framework Purpose-built To Close This Gap. The System Continuously Processes Surveillance Camera Videos Using A Fine-tuned YOLOv8 Deep Learning Model, Extracting Bounding Box Geometry, Posture Orientation, And Inter-frame Motion Vectors To Identify Anomalous Body Positions. A Temporal Classification Module Employing A 20-frame Confirmation Window At A 0.4 Confidence Threshold Reliably Distinguished Genuine Fall Events From Ordinary Work Postures, Such As Bending Or Crouching. Upon Confirmed Detection, Multichannel Emergency Alerts Are Dispatched Immediately: An Annotated Incident Image Via Email, An SMS To Registered Supervisors, And A Simultaneous Local Audio Alarm — All Without Human Intervention. The System Operates Effectively In Low-light And Dust-heavy Environments Through Dedicated Preprocessing, Requires No Wearable Devices, And Provides A Complete Incident Audit Trail, Representing A Substantive Advance Toward Reducing Preventable Fatalities In Isolated Mining Environments.

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Other Details

• Paper id:

  IJSARTV12I4104890

• Published in:

  Volume: 12 Issue: 4 April 2026

• Publication Date:

  2026-04-06

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