Pallet detection: Datasets & Models

Manually counting pallets, checking dock staging, and reconciling inventory against records is slow, error-prone, and pulls staff away from higher-value work. Automating pallet detection with computer vision turns existing camera infrastructure into a continuous source of ground-truth data about what is on the floor and where. Teams can flag misplaced loads, confirm outbound counts before a truck departs, and monitor slot utilization without physical walk-throughs. In high-throughput warehousing operations, this removes a persistent bottleneck between physical reality and warehouse management systems.

A trained detection model runs on the cameras and edge hardware you already have, so you can scale monitoring across docks, aisles, and staging areas without new sensors. Accurate, real-time pallet counts reduce shrinkage disputes and manual audits while feeding live data into inventory and yard management systems. Starting from an existing pallet dataset and fine-tuning RF-DETR shortens the path from prototype to a production-ready deployment.

1. Upload an image: Drop any photo into the widget above to try pallet detection for free, right on this page.
2. Preview instant results: The model detects pallet, stacked pallet, empty pallet, forklift, cardboard box 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.