Truck detection: Datasets & Models

In logistics and warehousing operations, tracking truck movement manually means staff watching gate cameras, filling out arrival logs, and reconciling dock schedules by hand, work that is slow, error-prone, and impossible to scale across multiple sites. Automating truck detection turns any existing camera into a sensor that timestamps arrivals, measures dwell time in the yard, and triggers gate or dock assignments automatically. This removes the guesswork from yard management and gives operations teams accurate, continuous visibility into vehicle flow. It also frees personnel from repetitive monitoring so they can focus on exceptions and throughput.

Teams get consistent, around-the-clock truck detection that integrates with the CCTV and gate cameras already installed on site, so there is no need for new hardware or costly infrastructure changes. Accurate detection feeds directly into yard management, dock scheduling, and access control systems, reducing wait times and manual logging errors. The approach scales from a single loading dock to a network of distribution centers using the same trained model.

1. Upload an image: Drop any photo into the widget above to try truck detection for free, right on this page.
2. Preview instant results: The model detects truck, trailer, semi truck, cargo container, forklift 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.