VideoGasNet: Deep learning for natural gas methane leak classification using an infrared camera

Abstract

Mitigating methane leakage from the natural gas system has become an increasing concern for climate change. Efficacious methane leak detection and classification can make mitigation more efficient and cost effective. Optical gas imaging (OGI) is widely used for the purpose of leak detection, but it does not directly provide quantitative detection results and leak sizes. In this study, we consider methane leak size classification as a video classification problem and develop deep learning models to classify the videos by leak volume. Firstly we collected the first methane leak video dataset - GasVid, which has ∼0.7 M frames of labeled videos of methane leaks from different leaking equipment, covering a wide range of leak sizes (5.3–2051.6 gCH4/h) and imaging distances (4.6–15.6 m). Secondly, we studied three deep learning algorithms, including 2D Convolutional Neural Networks (CNN) model, 3D CNN and the Convolutional Long Short Term Memory (ConvLSTM). We find that 3D CNN is the most accurate and robust architecture, and we name it VideoGasNet. The leak/non-leak binary detection accuracy approaches nearly 100%, and the highest small-medium-large classification accuracy is 78.2% with our 3D CNN network. In eight-class classification, accuracy drops to 39.1%, as this is a more challenging task. In summary, VideoGasNet greatly extends the capabilities of IR camera-based leak monitoring system from leak detection only to automated leak classification, and shows high accuracy for binary and three-class classification.