Parking space detection: Datasets & Models

Manually monitoring parking availability across garages, surface lots, and curbside zones is labor-intensive and impractical to scale across a smart city. Sensor-based systems that embed a device in every stall are expensive to install and maintain, whereas a single camera can cover dozens of spaces at once. Automating parking space detection with computer vision reuses existing surveillance infrastructure and delivers continuous, accurate occupancy data without per-space hardware. This lets traffic operators reduce circling, guide drivers to open spots, and inform dynamic signage and enforcement.

A vision model can monitor an entire lot from one camera, cutting the cost and complexity of stall-level sensors while scaling to garages, campuses, and municipal networks. It integrates with the cameras cities and operators already have, and RF-DETR provides strong detection accuracy on crowded, overlapping vehicle scenes. The result is real-time occupancy counts that plug directly into guidance apps, signage, and traffic management dashboards.

1. Upload an image: Drop any photo into the widget above to try parking space detection for free, right on this page.
2. Preview instant results: The model detects car, empty parking space, occupied parking space, parking line marking, truck within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.