Ball detection: Datasets & Models
Detect and localize balls in sports footage using an object detection model. Works on images and video frames to find the ball's position across a game or practice session.
Ball detection
This detection workflow locates the ball in sports footage, placing a bounding box around it in each image or video frame. It accepts inputs such as live camera feeds, broadcast video, or recorded clips, and can be trained on a ball dataset to track position across a game or practice session.
Try it out
Powered by general detection model
Run on custom image
Drop an image here or click to upload
Why automate ball detection
Manually tracking a ball across thousands of frames is tedious, slow, and impossible to scale across an entire season of footage. Automating ball detection with computer vision lets sports analytics teams generate positional data, possession stats, and trajectory maps without human annotation of every clip. This turns raw video into structured tracking data that feeds performance analysis, automated highlight generation, and broadcast graphics. For coaching staff and analysts, it means faster insight and consistent measurement across games.
Key benefits
Ball detection runs on existing broadcast cameras and sideline recordings, so there is no need for specialized tracking hardware. The workflow scales from a single practice clip to full-season archives and produces consistent, frame-accurate output that downstream analytics pipelines can consume directly. Starting from a labeled ball dataset and RF-DETR, teams can fine-tune quickly for their specific sport and camera angles.
How to use it
- Upload an image: Drop any photo into the widget above to try ball detection for free, right on this page.
- Preview instant results: The model detects soccer ball, basketball, tennis ball, baseball, volleyball 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 ball 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 ball detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Ball Detection project to Roboflow Agent.