Flare detection: Datasets & Models

Manual monitoring of flare stacks depends on operators watching camera banks or performing periodic site inspections, which misses intermittent events and cannot scale across dozens of remote assets. Automating flare detection with computer vision gives energy and utilities teams continuous, timestamped visibility into when flaring occurs, how long it lasts, and whether excessive smoke indicates incomplete combustion. This supports environmental compliance reporting, emissions tracking, and faster response to abnormal flaring that signals process upsets. It turns raw camera footage into structured event data without adding headcount at every site.

Teams gain automated, round-the-clock flare monitoring that integrates with existing CCTV, thermal cameras, and drone inspection programs rather than requiring new hardware. Detection runs at the edge or in the cloud and scales from a single stack to fleet-wide deployment across multiple facilities. Trained on a representative gas flare dataset with a model like RF-DETR, the system delivers consistent detection accuracy that feeds directly into compliance logs and operational dashboards.

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