Vehicle speed estimation: Datasets & Models

Manual vehicle speed detection relies on dedicated radar or inductive-loop hardware that is expensive to install, maintain, and expand across a road network. A vision-based approach lets traffic teams reuse existing surveillance and roadside cameras to measure speed, flag speeding vehicles, and gather flow data without trenching roads or deploying new sensors. This means faster rollout across intersections and corridors, and richer context since the same footage also captures vehicle type, lane occupancy, and congestion.

Detection models like RF-DETR run on standard camera feeds and integrate with existing traffic infrastructure, so you can scale speed estimation across many cameras without specialized hardware at each site. Teams get consistent, per-vehicle speed measurements alongside vehicle classification and count data, supporting enforcement, safety analysis, and adaptive signal control. The workflow scales from a single intersection to a citywide network as camera coverage grows.

1. Upload an image: Drop any photo into the widget above to try vehicle speed estimation for free, right on this page.
2. Preview instant results: The model detects car, truck, bus, motorcycle, license plate 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.