Road crack detection: Datasets & Models

Manual pavement inspection is slow, subjective, and hard to scale across the miles of roadway that infrastructure teams are responsible for maintaining. Sending crews to walk or drive segments and eyeball defects produces inconsistent severity ratings and creates a backlog that delays repairs until damage is expensive to fix. Automating crack detection with segmentation lets a team process thousands of images consistently, measure crack area and density objectively, and prioritize maintenance based on real data. This turns a subjective, labor-intensive survey into a repeatable pipeline that can run every time a vehicle drives a route.

Instance segmentation captures the exact shape and surface area of each crack, so teams get quantitative severity metrics instead of rough estimates, feeding directly into pavement condition indexes and maintenance planning. The workflow runs on imagery from cameras you already deploy on survey vehicles, drones, or phones, so there's no new hardware required to get started. As your labeled dataset grows, the model improves and scales to cover entire road networks without adding inspection headcount.

1. Upload an image: Drop any photo into the widget above to try road crack detection for free, right on this page.
2. Preview instant results: The model detects longitudinal crack, transverse crack, alligator crack, pothole, surface patch 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.