Parking occupancy detection: Datasets & Models

Manually counting available spaces or relying on inductive-loop and gate-counter sensors is expensive to install, hard to retrofit, and gives you only aggregate totals rather than which specific stalls are free. A vision-based approach works with cameras you may already have and can cover dozens of spaces per unit, cutting per-space hardware and maintenance costs. For municipalities and operators, accurate real-time occupancy reduces the time drivers spend circling for parking, eases congestion, and enables dynamic pricing or guidance without trenching sensors into pavement. It also gives you an auditable image record for disputes and analytics.

You get per-space occupancy status rather than just a running total, using existing camera infrastructure and standard detection models. The approach scales from a single lot to city-wide deployments by adding cameras and reusing the same model, and RF-DETR offers strong accuracy on small, densely packed vehicles viewed at angles. Detections integrate cleanly with guidance signage, mobile apps, and traffic dashboards via API.

1. Upload an image: Drop any photo into the widget above to try parking occupancy detection for free, right on this page.
2. Preview instant results: The model detects car, empty parking space, truck, motorcycle, parking sign 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.