Pavement distress detection: Datasets & Models

Manual pavement distress surveys are slow, expensive, and subjective, requiring trained inspectors to walk or drive corridors and rate damage by eye, which produces inconsistent results across teams and seasons. Automating this with computer vision lets construction and infrastructure teams process miles of road imagery consistently, quantify crack length and pothole area objectively, and catch deterioration earlier before it becomes costly reconstruction. Segmentation masks give you defensible, repeatable measurements that support data-driven repair budgets and asset management reporting.

Segmentation delivers per-pixel severity and area measurements that bounding boxes cannot, enabling accurate pavement condition scoring and repair cost estimation. The model runs on imagery from existing survey vehicles, drones, and smartphones, so you can scale coverage across an entire road network without new hardware. Consistent automated detection reduces inspector labor, speeds up survey cycles, and standardizes distress reporting across jurisdictions.

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