Pothole Detection and Logging

Building a Real-Time Pothole Detection & GPS Logging System on Raspberry Pi

At Haloxion, we focus on delivering practical AI systems that solve real-world problems. This project involved creating an edge-based pothole detection solution that identifies damaged road surfaces in real time and logs their exact GPS coordinates—making it suitable for municipalities, civic bodies, and large-scale road surveys.

Understanding the Problem

Road maintenance teams often rely on manual inspection, which is slow, inconsistent, and difficult to scale. The client needed an automated, on-device system capable of:

• Detecting potholes using a camera feed
• Running inference directly on a Raspberry Pi
• Capturing GPS coordinates for every detection
• Storing results for later analysis
• Operating without internet or cloud dependency

The challenge was to build a compact, accurate system that works entirely at the edge.

How We Engineered the Solution

1. Custom Deep Learning Model

   We developed and trained a tailored pothole detection model using a curated dataset with precise annotations, optimized for Raspberry Pi’s limited compute resources.

2. Edge Deployment on Raspberry Pi 4B

   After benchmarking multiple approaches, we deployed the model using a lightweight inference pipeline designed for consistent real-time performance.

3. Camera Integration for Live Detection

   The Pi camera module was integrated to continuously analyze road surfaces while the device is in motion.

4. GPS-Based Location Logging

   A GPS module records the latitude and longitude of every detected pothole, enabling exact mapping of road defects.

5. Database & Retrieval Tools

   All detections—with timestamps, coordinates, and metadata—are logged into a structured database for later visualization or reporting.

Final Outcome

The deployed system successfully detects potholes in real time and logs their precise GPS locations without requiring internet connectivity. This provides city authorities and field teams with a scalable, automated way to map road quality and plan repairs efficiently.

Why This Matters

Automated road-condition monitoring reduces human effort, improves data accuracy, and enables smarter urban infrastructure planning. By combining edge AI with GPS tracking, this project demonstrates how compact hardware can deliver meaningful civic solutions.