An OGI Based Unattended Natural Gas Leak Detection System by Utilizing the Power of Machine Learning and Computer Vision

Hritu Raj, Gargi Srivastava

2025

Abstract

In a climate-constrained future, reducing natural gas emissions is essential to prevent undermining the environmental benefits of using natural gas over coal. Although optical gas imaging (OGI) is widely used for detecting natural gas leaks, it is often time-consuming and relies on human intervention for leak identification. This study presents an operator-less solution for automated leak detection using convolutional neural networks (CNNs). Our approach utilizes a dataset of natural gas leaks to train a CNN model for automated plume recognition. We begin by gathering 32 video clips labeled with gas leaks from the FLIR dataset, which covers a variety of leak sizes (50-1800 g/h) and video capture distances (4.2-18.3 m) .Two background removal techniques were applied to isolate the gas plumes. A modified CNN model, trained with a combination of natural gas and smoke images from Kaggle, was then used to detect the plumes in the video frames. Our trained model was evaluated against other algorithms based on optical flow, showing impressive performance. Our CNN model achieved an accuracy of 99% in detecting medium/large leaks and 94% for small leaks. This approach offers a promising method for high-accuracy natural gas leak identification in real-world OGI assessments.

Paper Citation

in Harvard Style

Raj H. and Srivastava G. (2025). An OGI Based Unattended Natural Gas Leak Detection System by Utilizing the Power of Machine Learning and Computer Vision. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 823-830. DOI: 10.5220/0013345600003905

in Bibtex Style

@conference{icpram25,
author={Hritu Raj and Gargi Srivastava},
title={An OGI Based Unattended Natural Gas Leak Detection System by Utilizing the Power of Machine Learning and Computer Vision},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={823-830},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013345600003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - An OGI Based Unattended Natural Gas Leak Detection System by Utilizing the Power of Machine Learning and Computer Vision
SN - 978-989-758-730-6
AU - Raj H.
AU - Srivastava G.
PY - 2025
SP - 823
EP - 830
DO - 10.5220/0013345600003905
PB - SciTePress