VideoGasNet: Deep learning for natural gas methane leak classification using an infrared camera
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DOI: 10.1016/j.energy.2021.121516
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- Wang, Jingfan & Tchapmi, Lyne P. & Ravikumar, Arvind P. & McGuire, Mike & Bell, Clay S. & Zimmerle, Daniel & Savarese, Silvio & Brandt, Adam R., 2020. "Machine vision for natural gas methane emissions detection using an infrared camera," Applied Energy, Elsevier, vol. 257(C).
- Zhang, Xiaochun & Myhrvold, Nathan P. & Hausfather, Zeke & Caldeira, Ken, 2016. "Climate benefits of natural gas as a bridge fuel and potential delay of near-zero energy systems," Applied Energy, Elsevier, vol. 167(C), pages 317-322.
- Magnus Gålfalk & Göran Olofsson & Patrick Crill & David Bastviken, 2016. "Making methane visible," Nature Climate Change, Nature, vol. 6(4), pages 426-430, April.
- Tom Wigley, 2011. "Coal to gas: the influence of methane leakage," Climatic Change, Springer, vol. 108(3), pages 601-608, October.
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- Dai Geng & Di Wang & Yushuang Li & Wei Zhou & Hanbing Qi, 2023. "Detection Stability Improvement of Near-Infrared Laser Telemetry for Methane Emission from Oil/Gas Station Using a Catadioptric Optical Receiver," Energies, MDPI, vol. 16(9), pages 1-16, April.
- Bi, Yubo & Wu, Qiulan & Wang, Shilu & Shi, Jihao & Cong, Haiyong & Ye, Lili & Gao, Wei & Bi, Mingshu, 2023. "Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning," Energy, Elsevier, vol. 284(C).
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Keywords
Natural gas; Methane emission; Deep learning; Computer vision; Optical gas imaging; Video classification;All these keywords.
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