Lane detection: Datasets & Models

Manually annotating or reviewing lane geometry across thousands of miles of roadway is slow, inconsistent, and impossible to scale for real-time driving or traffic analytics. A segmentation model automates lane detection frame by frame, handling faded markings, curved roads, and varying lighting conditions that trip up rule-based edge detection. For teams building ADAS features, lane-departure warnings, or infrastructure inventory tools, this turns raw video into structured lane data that downstream planning and analytics systems can act on immediately.

Pixel-accurate segmentation captures the true curvature and width of lanes, not just approximate boxes, which matters for path planning and compliance checks. The workflow runs on existing traffic cameras and dashcam hardware, so you can layer it onto current infrastructure without new sensor investments. It scales from a single test vehicle to a citywide camera network, giving traffic and mobility teams consistent lane data across every feed.

1. Upload an image: Drop any photo into the widget above to try lane detection for free, right on this page.
2. Preview instant results: The model detects lane marking, solid line, dashed lane divider, crosswalk, road edge 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 (instance segmentation) 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.