Cable detection: Datasets & Models

Manually locating and tracing cables for robotic manipulation is slow, error-prone, and hard to scale, since cables are flexible, deformable objects that don't hold a fixed pose like rigid parts. A vision model that segments each cable gives a robot the precise contour and endpoint information it needs to grasp, route, or inspect wiring without hand-coded fixtures for every configuration. This reduces misgrasps and rework on assembly lines and lets automation cells adapt to varied cable layouts. The result is more reliable pick-and-place, connector mating, and quality checks on wire harnesses.

Instance segmentation delivers per-cable masks that support accurate grasp planning and defect localization, not just coarse detection. The workflow runs on existing industrial cameras and integrates into robotic control and inspection pipelines, so teams can deploy without new specialized hardware. As product variants and cable configurations change, the model can be retrained and scaled across multiple stations and lines.

1. Upload an image: Drop any photo into the widget above to try cable detection for free, right on this page.
2. Preview instant results: The model detects cable, wire harness, connector, plug, ribbon cable 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.