Cyclist detection: Datasets & Models
Cyclist detection uses computer vision to locate and track people riding bicycles in traffic camera and roadside footage. It draws bounding boxes around cyclists so downstream systems can count, monitor, or trigger safety responses.
Cyclist detection
This detection workflow processes images and video from intersection cameras, roadside sensors, and vehicle-mounted feeds to locate people riding bicycles in traffic scenes. Trained on a cyclist dataset, it returns a bounding box and confidence score for each rider, separating cyclists from pedestrians and motor vehicles so downstream systems can count, track, or trigger safety responses.
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Why automate cyclist detection
Manually reviewing traffic footage to count cyclists or flag near-miss situations is slow, inconsistent, and impossible to scale across a city's camera network. Automating cyclist detection with computer vision gives traffic engineers continuous, objective data on where and when cyclists travel, which supports signal timing, bike lane planning, and collision analysis. For smart cities, this turns raw video into actionable safety and mobility metrics without adding staff. Detection also enables real-time alerts, such as warning drivers or adjusting signals when a cyclist enters an intersection.
Key benefits
Teams get consistent, round-the-clock cyclist counts and movement data that feed directly into traffic planning and safety systems. The models integrate with existing traffic and CCTV cameras, so there's no need to replace hardware, and detection scales from a single intersection to an entire municipal network. Training on RF-DETR delivers strong accuracy on small and partially occluded riders common in dense urban scenes.
How to use it
- Upload an image: Drop any photo into the widget above to try cyclist detection for free, right on this page.
- Preview instant results: The model detects cyclist, bicycle, helmet, pedestrian, car 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.
- 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.
- 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.
Recommended model for cyclist detection
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for cyclist detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Cyclist Detection project to Roboflow Agent.