femi-cloud/RoadAI

A pothole detection and road repair optimization system for Cotonou, Benin # RoadAI — Cotonou, Benin Pothole detection and road repair optimization system for the city of Cotonou. ## Features - **Citizen reporting** (`/`) — upload a road photo, run YOLOv8 pothole detection, click the map for GPS, save a report - **Mayor dashboard** (`/dashboard`) — reports ranked by danger score, zone heatmap, optimized repair route - **Danger score** per report and zone: `number of potholes × average bounding-box area (px²)` - **Repair route** — greedy priority algorithm (high danger ÷ distance to next stop) ## Stack | Layer | Technology | |----------|-------------------------------------------| | Frontend | React 18, Vite, React Router, Leaflet | | Backend | Python 3, FastAPI, Uvicorn | | AI | Ultralytics YOLOv8 + Hugging Face Hub | | Model | `keremberke/yolov8n-pothole-segmentation` | | Database | SQLite (`backend/roadai.db`) | ## Project structure ``` RoadAI/ ├── backend/ │ ├── main.py # FastAPI app, HF model load, /api/detect │ ├── detection.py # Box parsing, red annotations, danger score │ ├── database.py # SQLite reports │ ├── optimization.py # Greedy repair route │ ├── zones.py # Cotonou 5×5 zone grid │ ├── uploads/ # Original images (gitignored) │ ├── annotated/ # Annotated images (gitignored) │ └── models/ # Optional local weights (see models/README.md) ├── frontend/ │ └── src/ │ ├── pages/ # ReportPage, DashboardPage │ ├── components/ # CotonouMap, Layout │ └── api.ts # API client (port 8001) ├── start-backend.ps1 ├── start-frontend.ps1 └── README.md ``` ## AI model and detection At startup, `main.py` downloads and loads the pothole model from Hugging Face: ```python from huggingface_hub import hf_hub_download from ultralytics import YOLO model_path = hf_hub_download( repo_id="keremberke/yolov8n-pothole-segmentation", filename="best.pt", ) model = …