Fire detection: Datasets & Models

Manual fire watch relies on people noticing a problem or on point sensors that only trigger once heat or particulate reaches the detector, which often means precious minutes are lost. Automating fire detection with computer vision lets you monitor many camera views simultaneously and catch visible flames the moment they appear in frame, without waiting for smoke to physically reach a sensor. For security and surveillance teams already operating extensive camera networks, this turns passive recording into an active early-warning layer. The result is faster response, fewer missed events, and coverage of large or remote areas that would be impractical to staff.

You can deploy fire detection on the cameras and infrastructure you already have, avoiding the cost and disruption of new hardware. Detection runs continuously and scales from a single site to hundreds of feeds, with alerts routed into the tools your operators already use. Because it identifies visible flames directly, it complements traditional smoke and heat sensors to shorten the gap between ignition and response.

1. Upload an image: Drop any photo into the widget above to try fire detection for free, right on this page.
2. Preview instant results: The model detects fire, flame, smoke 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.