Project
MLOps-Enabled Pothole Detection System
Year
2025
Service
Computer Vision
Pothole Detection System
Project Overview
This project is an intelligent, real-time pothole detection and mapping platform that combines computer vision, AI, and MLOps to improve road safety and infrastructure maintenance. It processes live camera feeds to identify potholes, analyzes their severity, and displays results on an interactive map. The system also automates model training, deployment, and monitoring to ensure continuous performance improvement at scale.
Project Overview
This project is an intelligent, real-time pothole detection and mapping platform that combines computer vision, AI, and MLOps to improve road safety and infrastructure maintenance. It processes live camera feeds to identify potholes, analyzes their severity, and displays results on an interactive map. The system also automates model training, deployment, and monitoring to ensure continuous performance improvement at scale.
The Problem
Manual road inspections and traditional reporting systems are slow, costly, and unable to keep up with rapidly changing road conditions. Delays in detecting and repairing potholes increase the risk of accidents, vehicle damage, and inefficient use of municipal resources. Additionally, AI models used in detection often degrade over time due to changing environments, leading to reduced accuracy and unreliable results.
The Problem
Manual road inspections and traditional reporting systems are slow, costly, and unable to keep up with rapidly changing road conditions. Delays in detecting and repairing potholes increase the risk of accidents, vehicle damage, and inefficient use of municipal resources. Additionally, AI models used in detection often degrade over time due to changing environments, leading to reduced accuracy and unreliable results.
The Solution
The platform uses AI-powered computer vision to automatically detect potholes in real time from live camera feeds and assigns severity levels using intelligent analysis. An integrated MLOps pipeline continuously retrains, versions, and deploys models based on new data while monitoring performance and drift. This ensures reliable detection, faster response times, and a scalable system that adapts to evolving road conditions.
The Solution
The platform uses AI-powered computer vision to automatically detect potholes in real time from live camera feeds and assigns severity levels using intelligent analysis. An integrated MLOps pipeline continuously retrains, versions, and deploys models based on new data while monitoring performance and drift. This ensures reliable detection, faster response times, and a scalable system that adapts to evolving road conditions.
The Result
The system delivers faster and more accurate pothole detection, real-time visibility through an interactive map, and improved maintenance prioritization for authorities. Continuous model optimization maintains high detection performance over time, reducing accidents, lowering repair costs, and enabling data-driven infrastructure management.
The Result
The system delivers faster and more accurate pothole detection, real-time visibility through an interactive map, and improved maintenance prioritization for authorities. Continuous model optimization maintains high detection performance over time, reducing accidents, lowering repair costs, and enabling data-driven infrastructure management.
