Crane detection: Datasets & Models

On active construction and infrastructure sites, tracking crane presence, movement, and proximity to workers by hand is slow, error-prone, and impossible to scale across many camera feeds. Manually reviewing hours of footage or relying on spotters introduces gaps that can lead to missed safety violations and inaccurate progress reporting. Automating crane detection with computer vision gives you consistent, round-the-clock coverage, letting you flag equipment activity, enforce exclusion zones, and log usage without dedicating staff to watch every screen. The result is faster incident response and a reliable record of when and where cranes are operating.

Automated crane detection integrates with the cameras, drones, and CCTV infrastructure you already have, so there's no need for specialized sensors on every machine. It scales from a single site to an entire portfolio of projects, running detection across many feeds simultaneously and producing structured data for safety audits, progress tracking, and utilization reports. With RF-DETR as the recommended model, you get accurate detection that holds up across varied lighting, weather, and camera angles common on construction sites.

1. Upload an image: Drop any photo into the widget above to try crane detection for free, right on this page.
2. Preview instant results: The model detects tower crane, mobile crane, crane boom, crane hook, construction load 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.