The latest articles on DEV Community by Aditya Verma (@adityaverma9777). https://dev.to/adityaverma9777 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4190007%2F2e95b70f-d009-49e2-8365-9e84a3836211.jpg https://dev.to/adityaverma9777 en Aditya Verma Sat, 17 Oct 2026 07:07:53 +0000 https://dev.to/adityaverma9777/streetiq-ai-powered-road-intelligence-for-safer-navigation-8d1 https://dev.to/adityaverma9777/streetiq-ai-powered-road-intelligence-for-safer-navigation-8d1 <h1> StreetIQ: AI-Powered Road Intelligence for Safer Navigation </h1> <h2> What I Built </h2> <p>StreetIQ is an AI-powered road intelligence and navigation platform designed to make roads safer by detecting and mapping hazards such as potholes, cracks, waterlogging, and debris.</p> <p>Road hazards are often reported late, inconsistently, or not at all. StreetIQ aims to turn these scattered observations into actionable road intelligence.</p> <p>The platform combines computer vision with real-time mapping and community-driven reporting. Its key features include:</p> <ul> <li> <strong>AI-powered hazard detection:</strong> A custom YOLOv8 model detects road hazards, with TensorFlow.js enabling browser-based inference.</li> <li> <strong>Real-time hazard mapping:</strong> Displays road hazards on an interactive map using OpenStreetMap-based mapping tools.</li> <li> <strong>Navigation and warnings:</strong> Integrates routing and location services to help users understand road conditions along their routes.</li> <li> <strong>Crowdsourced reporting:</strong> Allows users to contribute hazard reports, including voice-assisted reporting.</li> <li> <strong>Hazard verification and updates:</strong> Supports verification workflows and real-time updates to improve the usefulness of road intelligence.</li> <li> <strong>Road intelligence for authorities:</strong> The long-term goal is to help municipalities and road maintenance teams identify problem areas and prioritize repairs.</li> </ul> <p><strong>Who could pay for it?</strong></p> <p>Potential customers include municipal authorities, logistics and fleet operators, navigation providers, and road maintenance organizations. StreetIQ could evolve into a road-condition intelligence service that helps these organizations reduce vehicle damage, improve route planning, and prioritize infrastructure maintenance.</p> <p>The project is still evolving, and these are potential commercial applications rather than claims of existing paying customers.</p> <h2> Demo </h2> <p>Watch the StreetIQ demo:</p> <p><a href="https://www.youtube.com/watch?v=lJVnyaPadkw" rel="noopener noreferrer">https://www.youtube.com/watch?v=lJVnyaPadkw</a></p> <h2> Code </h2> <p><strong>GitHub repository:</strong> <a href="https://github.com/adityaverma9777/StreetIQ" rel="noopener noreferrer">https://github.com/adityaverma9777/StreetIQ</a></p> <p>The repository contains the implementation and development work behind StreetIQ. Contributions, feedback, and improvements to its detection pipeline, mapping, and reporting workflows are welcome.</p> <h2> How I Built It </h2> <p>I built StreetIQ using an open-source computer-vision approach combined with a web-based mapping and real-time backend.</p> <p><strong>Technology stack:</strong></p> <ul> <li> <strong>AI and computer vision:</strong> Custom YOLOv8 model with TensorFlow.js for browser-based road-hazard detection.</li> <li> <strong>Frontend:</strong> React, Vite, and React-Leaflet.</li> <li> <strong>Backend:</strong> Node.js, Express, and TypeScript.</li> <li> <strong>Database and geospatial data:</strong> Supabase PostgreSQL with PostGIS.</li> <li> <strong>Real-time communication:</strong> WebSockets and Supabase Realtime.</li> <li> <strong>Maps and routing:</strong> OpenStreetMap, OSRM, Photon, and Komoot geocoding services.</li> </ul> <p>The core detection workflow uses a YOLOv8 model that can run in the browser through TensorFlow.js. This makes it possible to perform visual inference without sending every image frame to a remote inference server.</p> <p>Detected hazards can then be connected to the wider platform through location-aware reporting, geospatial storage, mapping, and real-time updates.</p> <p>The engineering challenge was not just detecting a pothole in an image. It was connecting detection with geographic context, useful reporting, navigation, and a system that can eventually support large-scale road-condition monitoring.</p> <h2> Why Does Open Innovation Matter? </h2> <p>Road safety is a problem that affects everyone,</p> devchallenge hf26challenge