AI-Powered Pavement Inspection Web Platform - (MVP Build)

Hello Upwork Freelancers, We're looking for a talented full-stack AI developer to help build SlurryBird - a cloud-based pavement inspection and reporting platform designed for civil engineering firms, municipalities, and contractors. Traditional pavement inspection is highly manual: field teams photograph roadway conditions, document defects by hand, and compile reports using spreadsheets and drafting tools. SlurryBird automates the early-stage inspection and documentation workflow using AI-assisted image analysis, replacing hours of manual review with annotated outputs and downloadable reports. We have an existing MVP codebase built on OpenCV with a DetectionProvider abstraction designed for YOLO/Roboflow swap-in. We're looking to bring a developer on board within two weeks to extend and productionize this into a fully functional web portal. WHAT THE PLATFORM DOES Accepts uploaded drone imagery, roadway photos, or pavement footage Runs AI detection (YOLO-based) to identify visible pavement defects: cracks, potholes, faded lane markings, curb edge conditions, surface deterioration Generates annotated imagery and defect summaries, including PCI scoring Produces downloadable inspection reports in PDF and CSV formats Provides a lightweight cloud dashboard for project upload, review, and export Allows users to flag or dismiss individual detections before finalizing - AI output is never treated as final without human review SCOPE OF WORK (MVP) Extend the existing OpenCV/DetectionProvider codebase to integrate YOLO or Roboflow for crack and defect detection Build a cloud-based web portal with project upload, AI output review, and report export functionality Implement PDF and CSV report generation with annotated screenshots and issue summaries Configure and deploy on a provided hosting environment (Digital Ocean droplet) Maintain version-controlled code on a provided GitHub account Write a Wiki or README documenting the codebase logic for future development teams Note: GIS integration, CAD export, drone operations management, billing, and LiDAR ingestion are intentionally out of scope for the MVP. RESOURCES WE PROVIDE Sample pavement inspection images across various roadway conditions and defect types JPEG/PNG files of standard highway and pavement-related signage Engineering pavement inspection references and PCI rating methodology documentation Existing MVP codebase (OpenCV + DetectionProvider abstraction) Digital Ocean droplet for hosting GitHub account for version control WHAT WE'RE LOOKING FOR Experience with Python, computer vision (OpenCV, YOLO, or Roboflow), and cloud deployment Ability to build clean, functional web portals (frontend + backend) PDF/CSV report generation experience a plus Strong communication and documentation habits Ability to provide a cost estimate and development schedule Please include relevant portfolio examples with your proposal. Auto-replies that do not reference the work scope described above will not be considered.