Ai Powered Smart Pavement Health Monitoring Using Uav And Deep Learning

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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