Gas leak detection: Datasets & Models

Manual gas leak detection depends on scheduled walkdowns, handheld sniffers, and human review of OGI footage, slow, intermittent, and prone to missed events between inspection cycles. In energy and utility environments, an undetected leak is a safety, regulatory, and financial risk, and around-the-clock human monitoring of dozens of camera feeds is impractical. Automating detection with segmentation lets you continuously watch pipelines, compressor stations, and storage yards, flag emissions the moment a plume appears, and pinpoint exactly which component is venting. That shifts the workflow from periodic spot checks to persistent, automated surveillance.

Instance segmentation delivers pixel-accurate masks that distinguish gas plumes from steam, shadows, and background clutter, reducing false alarms while localizing the leak source. The workflow runs on existing fixed cameras, OGI feeds, and inspection drones, so teams can scale monitoring across many assets without new field hardware. Alerts can be routed into existing SCADA or incident-response systems for faster containment and cleaner regulatory documentation.

1. Upload an image: Drop any photo into the widget above to try gas leak detection for free, right on this page.
2. Preview instant results: The model detects gas plume, vapor cloud, pipeline, valve, pressure vessel 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 (instance segmentation) 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.