Shopping cart detection: Datasets & Models

Manually tracking where shopping carts end up, abandoned in aisles, stranded in parking lots, or missing from corrals, is tedious and error-prone, and staff time spent retrieving and counting carts adds up quickly. Automating cart detection with computer vision lets retail tech teams monitor availability in real time, flag carts left outside designated zones, and reduce loss from carts leaving the property. Because the model runs against footage from existing surveillance and lot cameras, you get continuous visibility without stationing employees to watch inventory.

Building on a shopping cart dataset lets you train a detector that plugs directly into cameras and infrastructure you already operate, avoiding new hardware costs. The RF-DETR architecture offers strong accuracy on cluttered retail scenes and scales from a single storefront to fleets of locations. You can iterate quickly by adding labeled images from your own environment to sharpen performance on your specific cart models and camera angles.

1. Upload an image: Drop any photo into the widget above to try shopping cart detection for free, right on this page.
2. Preview instant results: The model detects shopping cart, shopping basket, cart corral, person 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 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.