AI-Based Wrong-Way Vehicle Detection System

Backend: FastAPI + OpenCV + Ultralytics YOLOv8 (ByteTrack).
Frontend: React (Vite), dark UI, upload → processed video + dashboard + violations + lane changes.

No database; violation images are saved under backend/violators/. Upload returns JSON with video_url, total_tracked_vehicles, wrong_way_count, violations, and lane_changes.

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Run backend

cd Backend
python -m venv .venv
.venv\Scripts\activate   # Windows
# source .venv/bin/activate   # macOS/Linux
pip install fastapi uvicorn opencv-python ultralytics imageio python-multipart
uvicorn main:app --reload --host 0.0.0.0 --port 8000

• API: http://localhost:8000
• Processed video: GET /video
• Violation images: GET /violators/<filename>
• Upload: POST /upload/ (body: multipart file)

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Run frontend

cd Frontend
npm install
npm run dev

• App: http://localhost:5173 (or the port Vite prints)
• Open Dashboard → Wrong-Way Detection, upload a video, then view processed video, stats, violation cards, and lane-change list.

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Project layout

WrongWayAI/
  Backend/
    main.py          # Single FastAPI app, no DB
    violators/       # Violation images (created automatically)
  Frontend/
    src/
      App.tsx
      pages/WrongWayDetection.tsx
      components/wrongway/Upload.tsx, Dashboard.tsx, Violations.tsx, LaneChanges.tsx
      services/api.ts