Ai Powered Smart Pavement Health Monitoring Using Uav And Deep Learning

• Author(s):

  Dr Gajendran Chellaiah | Kavitan P | Terrin Jerold E

• Keywords:

  UAV Technology, Deep Learning, Pavement Monitoring, CNN, YOLOv5, Smart Cities, Infrastructure Management

• Abstract:

  Urban Road Networks Require Frequent Maintenance To Remain Operational, Safe, And Sustainable. As Cities Expand, The Scale Of Inspection Needed Becomes Infeasible Through Traditional Manual Survey Methods. This Paper Introduces A Smart Pavement Health Monitoring System Using Unmanned Aerial Vehicles (UAVs) Integrated With Deep Learning Algorithms For Autonomous Defect Detection And Classification. The System Employs High-resolution Camera-equipped Drones To Capture Comprehensive Pavement Imagery Through Automated Flight Patterns, Processed By A Custom-trained Convolutional Neural Network (CNN) Based On YOLOv5 Architecture To Detect, Classify, And Map Cracks, Potholes, And Surface Defects. Unlike Conventional Inspection Methods, This Approach Offers A Cost-effective, Scalable Solution Capable Of Delivering Real-time Data For Preventive Maintenance Strategies. Results Demonstrate That The AI-based Detection Model Achieves 91.3% Accuracy With Minimal False Positives. The UAV-based System Reduces Inspection Time By 65-75% Compared To Traditional Surveys While Maintaining Superior Data Quality. The Integrated GIS Dashboard Provides Municipal Authorities With Real-time Visualization And Automated Alert Systems. Field Testing Across Urban And Semi-urban Networks Validates The Framework's Effectiveness For Smart City Infrastructure Integration.

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Other Details

• Paper id:

  IJSARTV11I7103902

• Published in:

  Volume: 11 Issue: 7 July 2025

• Publication Date:

  2025-07-21

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