# Machine Vision for Natural Gas Methane Emissions Detection Using an Infrared Camera

arXiv: https://arxiv.org/abs/1904.08500 · HTML: https://mlsanity.com/paper/1904.08500/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 8 of 8)

- [Gasformer: A Transformer-based Architecture for Segmenting Methane Emissions from Livestock in Optical Gas Imaging](https://mlsanity.com/paper/2404.10841/) (2024)
- [LangGas: Introducing Language in Selective Zero-Shot Background Subtraction for Semi-Transparent Gas Leak Detection with a New Dataset](https://mlsanity.com/paper/2503.02910/) (2025)
- [Invisible Gas Detection: An RGB-Thermal Cross Attention Network and A New Benchmark](https://mlsanity.com/paper/2403.17712/) (2024)
- [GasTwinFormer: A Hybrid Vision Transformer for Livestock Methane Emission Segmentation and Dietary Classification in Optical Gas Imaging](https://mlsanity.com/paper/2508.15057/) (2025)
- [Vision-Ultrasound Robotic System based on Deep Learning for Gas and Arc Hazard Detection in Manufacturing](https://mlsanity.com/paper/2502.05500/) (2025)
- [Multi-source Plume Tracing via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2505.08825/) (2025)
- [JVLGS: Joint Vision-Language Gas Leak Segmentation](https://mlsanity.com/paper/2508.19485/) (2025)
- [Edge-Aware and Content-Adaptive Infrared Gas Leak Detection for Industrial Safety Monitoring](https://mlsanity.com/paper/2512.23234/) (2025)

## Abstract (arXiv)

It is crucial to reduce natural gas methane emissions, which can potentially offset the climate benefits of replacing coal with gas. Optical gas imaging (OGI) is a widely-used method to detect methane leaks, but is labor-intensive and cannot provide leak detection results without operators' judgment. In this paper, we develop a computer vision approach to OGI-based leak detection using convolutional neural networks (CNN) trained on methane leak images to enable automatic detection. First, we collect ~1 M frames of labeled video of methane leaks from different leaking equipment for building CNN model, covering a wide range of leak sizes (5.3-2051.6 gCH4/h) and imaging distances (4.6-15.6 m). Second, we examine different background subtraction methods to extract the methane plume in the foreground. Third, we then test three CNN model variants, collectively called GasNet, to detect plumes in videos taken at other pieces of leaking equipment. We assess the ability of GasNet to perform leak detection by comparing it to a baseline method that uses optical-flow based change detection algorithm. We explore the sensitivity of results to the CNN structure, with a moderate-complexity variant performing best across distances. We find that the detection accuracy can reach as high as 99%, the overall detection accuracy can exceed 95% for a case across all leak sizes and imaging distances. Binary detection accuracy exceeds 97% for large leaks (~710 gCH4/h) imaged closely (~5-7 m). At closer imaging distances (~5-10 m), CNN-based models have greater than 94% accuracy across all leak sizes. At farthest distances (~13-16 m), performance degrades rapidly, but it can achieve above 95% accuracy to detect large leaks (>950 gCH4/h). The GasNet-based computer vision approach could be deployed in OGI surveys to allow automatic vigilance of methane leak detection with high detection accuracy in the real world.