Forklift detection: Datasets & Models

Manually monitoring forklift traffic across a warehouse is impractical: staff cannot watch every aisle and dock camera at once, and near-misses between forklifts and pedestrians are easy to miss until an incident occurs. Automating forklift detection lets you count vehicles, measure dwell time in specific zones, and trigger alerts when a forklift enters a restricted or high-foot-traffic area. For logistics and warehousing operations, this turns existing surveillance footage into an actionable safety and throughput signal without adding headcount.

Teams can deploy on cameras already installed on the floor, avoiding new hardware investment, and scale detection across multiple sites from a single trained model. Detections integrate cleanly with alerting, access control, and analytics systems to enforce safety zones and quantify equipment utilization. Training on a labeled forklift dataset with a modern architecture like RF-DETR delivers reliable performance in cluttered, variable-lighting warehouse environments.

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