Remote Gas Detection System Using Infrared Camera Technology and Sophisticated Gas Plume Detection Computer Algorithm

Abdel-Moati, Hazem; Morris, Jonathan M.; Ruan, Yunxing; Zeng, Yan

doi:10.2118/166438-ms

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…This product makes use of the temporal motion of the plume and the apparent temperature difference between the hydrocarbon plume and the background to detect the leak. It has a multi-stage algorithm to confirm an emission event [30]. Another company, Rebellion Photonics, uses a more sophisticated hyper-spectral imaging camera that can distinguish between different gas species and claims to quantify the leak size in real time [31].…”

Section: Related Workmentioning

confidence: 99%

Machine vision for natural gas methane emissions detection using an infrared camera

et al. 2020

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In a climate-constrained world, 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 training, validating and testing CNN model, covering a wide range of leak sizes (5.3-2051.6 gCH 4 /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 (fraction of leak and non-leak images correctly identified by the algorithm) can reach as high as 99%, the overall detection accuracy can exceed 95% for a case across all leak sizes and imaging distances. Binary 1 arXiv:1904.08500v1 [cs.CV] 1 Apr 2019 detection accuracy exceeds 97% for large leaks (∼710 gCH 4 /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 gCH 4 /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.

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Section: Related Workmentioning

confidence: 99%

Machine vision for natural gas methane emissions detection using an infrared camera

et al. 2020

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“…Weiterhin lassen sich durch zeitliche Filterung Veränderungen in Bildsequenzen erkennen und damit z. B. Bewegungen oder Gas detektieren[21,22].…”

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Übersicht zu Sensordatenfusionsansätzen in der Thermografie

Soldan¹,

Kroll²

2014

Tm - Technisches Messen

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Zusammenfassung:In diesem Beitrag wird eine Übersicht über verschiedene Ansätze gegeben, um durch Fusion bzw. Kombination von mehreren, unterschiedlichen Aufnahmen die Qualität des thermografischen Messergebnisses zu verbessern und/oder die Menge an nutzbaren Informationen über ein Messobjekt zu erhöhen. Dabei wird sowohl die Kombination verschiedener Thermografiedaten (uni-modale Fusion), als auch die Sensordatenfusion mit anderen bildgebenden Messverfahren (multi-modale Fusion) behandelt.Schlüsselwörter: Thermografie, Sensordatenfusion, Kamerakalibrierung, Registrierung, Datenverdichtung. Abstract:In this article an overview of various approaches to improve the quality of thermographic measurements and to increase the amount of useable information about a remotely sensed object is given. This is done by combining multiple images from the same thermal imager (unimodal fusion) as well as by sensor data fusion with other imaging instruments (multi-modal fusion).

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

Wang¹,

Tchapmi²,

Ravikumara³

et al. 2019

Preprint

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Remote Gas Detection System Using Infrared Camera Technology and Sophisticated Gas Plume Detection Computer Algorithm

Cited by 3 publications

References 3 publications

Machine vision for natural gas methane emissions detection using an infrared camera

Machine vision for natural gas methane emissions detection using an infrared camera

Übersicht zu Sensordatenfusionsansätzen in der Thermografie

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

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