Traffic detection: Datasets & Models

Manually reviewing traffic footage or relying on inductive-loop sensors gives you limited, expensive, and location-locked data that misses vehicle class, direction, and behavior. Computer vision turns existing CCTV and traffic cameras into rich sensors that count vehicles, measure queue length, and flag congestion or wrong-way movement automatically, around the clock. For smart cities and transportation agencies, that means signal timing, planning studies, and enforcement can be driven by continuous, granular data instead of periodic manual counts. Automating detection also removes the labor bottleneck of watching hours of video to find the few frames that matter.

You get consistent, frame-by-frame detection of every road user without adding new roadside hardware, since the model runs against the camera infrastructure most municipalities already have. Detection outputs plug directly into downstream counting, tracking, and analytics pipelines, and the workflow scales from a single intersection to a citywide network. Starting from a labeled traffic dataset and the recommended RF-DETR model, teams can fine-tune quickly on their own camera angles and lighting conditions.

1. Upload an image: Drop any photo into the widget above to try traffic detection for free, right on this page.
2. Preview instant results: The model detects car, truck, bus, motorcycle, pedestrian, bicycle 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.