Real-Time Road Surface Damage Detection

Problem statement

Road defects such as potholes and cracks significantly increase accident risk and vehicle damage. Manual inspection of roads is slow, infrequent, and prone to human error. There is a need for an automated, real-time system that can detect and classify road defects for smart city infrastructure and government maintenance planning.

Abstract

This project implements a deep-learning-based road inspection system that uses a camera feed from a dashboard camera or smartphone mounted on a vehicle. A convolutional neural network identifies road defects such as potholes, cracks, and uneven surfaces. Each detection is tagged with GPS coordinates and sent to a cloud dashboard for visualization. The system reduces manual effort and speeds up road maintenance by generating dynamic heatmaps of high-damage areas.

Components required

• Raspberry Pi / Jetson Nano / Laptop
• USB/HD Camera Module
• GPS Module (NEO-6M)
• TensorFlow or PyTorch
• OpenCV
• Cloud dashboard (Firebase / Node.js / custom server)

Block diagram

Working

A camera mounted on a moving vehicle captures road frames continuously. The frames are resized, normalized, and passed to a trained CNN model capable of identifying potholes, cracks, or bumps. Whenever damage is detected, the system retrieves GPS coordinates from the GPS module and stores both the image and the location in the cloud database. A dashboard displays all detected defects on a map, enabling authorities to prioritize repairs.

Applications

• Smart city road monitoring
• Government road maintenance departments
• Fleet management companies
• Automated insurance claim assessment
• Research on road infrastructure quality