Machine vision for natural gas methane emissions detection using an infrared camera
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DOI: 10.1016/j.apenergy.2019.113998
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Cited by:
- Shuo Sun & Linwei Ma & Zheng Li, 2021. "Methane Emission Estimation of Oil and Gas Sector: A Review of Measurement Technologies, Data Analysis Methods and Uncertainty Estimation," Sustainability, MDPI, vol. 13(24), pages 1-29, December.
- Mark Agerton & Ben Gilbert & Gregory B. Upton Jr., 2021. "The Economics of Natural Gas Venting, Flaring and Leaking in U.S. Shale: An Agenda for Research and Policy," Working Papers 2021-02, Colorado School of Mines, Division of Economics and Business.
- Shi, Zhongtuo & Yao, Wei & Zeng, Lingkang & Wen, Jianfeng & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu, 2020. "Convolutional neural network-based power system transient stability assessment and instability mode prediction," Applied Energy, Elsevier, vol. 263(C).
- Titchener, James & Millington-Smith, Doug & Goldsack, Chris & Harrison, George & Dunning, Alexander & Ai, Xiao & Reed, Murray, 2022. "Single photon Lidar gas imagers for practical and widespread continuous methane monitoring," Applied Energy, Elsevier, vol. 306(PB).
- Wang, Jingfan & Ji, Jingwei & Ravikumar, Arvind P. & Savarese, Silvio & Brandt, Adam R., 2022. "VideoGasNet: Deep learning for natural gas methane leak classification using an infrared camera," Energy, Elsevier, vol. 238(PB).
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Keywords
Natural gas; Methane emission; Deep learning; Convolutional neural network; Computer vision; Optical gas imaging;All these keywords.
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