We are building a lightweight MVP to detect potholes & road defects from regular mobile-mounted videos (similar to RoadMetrics). The system will be used by delivery-partners (Zomato/Swiggy-style) who automatically capture road conditions during rides. Scope of Work We need an AI/ML + backend developer to build: 1. AI / Computer Vision Model Detect potholes, cracks, rough patches from dashboard-style video (1080p/720p) You can use YOLOv8/YOLOv11/Detectron2/RT-DETR or any optimized model Output required: Pothole bounding boxes Severity score GPS tag (from metadata or manual input) JSON summary 2. Backend + API Simple Python API (FastAPI preferred) Accepts uploaded videos Returns processed JSON + snapshot images Store data in lightweight DB (SQLite or Firebase) 3. Simple Dashboard (optional if you can do frontend) View processed data Map with pothole markers Download JSON/CSV You Don’t Need to Build a Full App Just MVP: upload → detect → output. Skills Needed Python + FastAPI Computer Vision (YOLO/Segmentation/Video processing) PyTorch / TensorFlow Basic cloud deployment (AWS/Linode/VPS) Budget ₹60,000 – ₹90,000 (fixed price) Paid in milestones. Small paid test-task will be required (detect 5 potholes in sample video). What to Include in Your Proposal Past work in computer vision (especially object detection) Model you plan to use Delivery timeline Links to GitHub or portfolio