IP Library Granted Patent US 12,392,681
Granted Patent B2
US 12,392,681 · App. 17/845,888 · Granted Aug 19, 2025

System and method for remote detection and location of gas leaks

Inventors: Mary D. O'Neill (Santa Barbara, CA); Mark Morey (Las Vegas, NV); David Terry (Santa Barbara, CA)
Assignee: MISSION SUPPORT AND TEST SERVICES, LLC
G01M3/38G01M3/04G06F18/23G06T7/246G06V10/147G06V10/762G06V20/52G08B21/12H04N5/272H04N7/183H04N23/56G06T2207/30232
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Quick Facts
Patent No.
US 12,392,681
App. No.
17/845,888
Granted
Aug 19, 2025
Kind
B2
Abstract

A system for monitoring for a gas leak from a gas containing structure. The system includes a lens that directs an image of a scene of interest through an optical filter to a detector. The filter, associated with the lens, has one or more passbands that pass wavelengths which match one or more emission or reflectively wavelengths of the gas being monitored. A detector receives the image after the image passes through the lens and the filter. The detector generates image data representing the scene including the gas containing structure. A processor is configured to process the image data by executing machine executable code stored on a memory. The machine executable code processes the image data to identify turbulence flows in the image data such that a turbulence flow indicates a gas leak. The code generates and sends an alert in response to the identification of a turbulence flow.

Claims (43)

1. A system for monitoring for a gas leak from a gas containing structure comprising:

a lens configured to receive an image of a scene of interest, the scene of interest containing a gas containing structure containing a gas being monitored;

a filter located after the lens, the filter having one or more passbands that pass wavelengths which match one or more emission or reflectively wavelengths of the gas being monitored;

a detector arranged to receive the image after the image passes through the lens and the filter, the detector configured to generate image data representing the scene including gas containing structure, wherein the detector is not actively cooled;

a processor configured to process the image data by executing machine executable code;

a memory configured to store non-transitory machine executable code, the machine executable code configured to:

process the image data to identify turbulence flow in the scene represented by the image data such that turbulence flow indicates a gas leak, the processing of the image data comprising;

calculating normalized variance over all pixels of the image data to create flux variance images;

processing the flux variance images by computing an angle of gas flow for pixels and computing a magnitude of gas flow for pixels;

identify flow patterns based on the angle of gas flow and the magnitude of gas flow, the flow patterns indicating the turbulence flow; and

generate and send an alert in response to identification of a turbulence flow.

2. The system of claim 1 wherein the gas containing structure is selected from the following group of gas containing structures: pipe, pipeline, tank, barrel, hose, tanker, container, or any structure that contains a gas needing to be monitored.

3. The system of claim 1 wherein process the image data further comprises superimposing the turbulence flow on a context image to create an alert image and sending the alert image with the alert.

4. The system of claim 1 wherein calculating the normalized variance over all pixels of the image data comprises calculating flux variance of a frame stack to enhance turbulence to create the flux variance images.

5. The system of claim 4 further comprising, using a clustering algorithm to cluster pixels represented by the image data that are above a signal to noise threshold and analyze cluster objects for flow patterns indicating gas flow.

6. The system of claim 1 further comprising a communication module configured with a wired or wireless transceiver, the communication module configured to send the alert to provide notification of the gas leak.

7. A method for monitoring for a gas leak from a gas containment structure containing a gas comprising:

providing a gas leak monitoring system that includes a lens, filter, and detector, which form a sensor, and also a processor, and a memory;

directing the sensor at a scene to receive emitted or reflected image wavelengths from the scene, the scene comprising at least a portion of the gas containment structure and an area around the gas containment structure;

filtering the scene with the filter located in the sensor, the filter having a passband that corresponds to an emission band or reflection band of the gas;

capturing image data with the sensor, the image data representing the scene after filtering by the filter;

processing the image data to identify turbulence, indicating gas flow, based on variation in pixel values over time, which includes;

calculating normalized variance over all pixels of the image data to create flux variance images;

identifying pixels represented by the image data that are above a signal to noise threshold and analyzing the image data for flow patterns indicating gas flow, the flow patterns identified by processing the flux variance images; and

responsive to identification of turbulence, generate and send an alert indicating a gas leak.

8. The method of claim 7 further comprising providing a lens and adjusting the lens to control the portion of the gas containment structure and the area around the gas containment structure that is presented to the filter and detector.

9. The method of claim 7 further comprising removing false object movement based on average flow direction.

10. The method of claim 7 wherein processing the image data further comprises removing or disregarding object movement unrelated to gas leaks from the image data based on average flow direction of pixel data.

11. The method of claim 7 wherein sending an alert comprises sending an alert with an image of the gas leak overlaid on a context image.

12. A system for monitoring and detecting a release of a gas from a monitored area comprising:

a lens;

a filter, directed at the monitored area, having a passband that passes at least one emission wavelength or reflective wavelength of the gas, the filter generating a filtered image;

a detector configured to receive and generate image data representing the filtered image;

a communication module configured to communicate over a network;

a processor configured to execute machine executable instructions;

a memory configured to store machine executable instructions that:

process the image data to identify turbulence generated by a release of the gas based on changes in pixel values over time, wherein processing the image data to identify turbulence comprises identifying flow patterns indicating gas flow, wherein the flow patterns are computed via derivatives of intensity variations with respect to image rows and columns of the image data;

generate an alert configured to notify a person of the release of the gas; and

send the alert to the person.

13. The system of claim 12 wherein the lens is configured to focus the release of the gas in the filtered image onto the detector.

14. The system of claim 12 further comprising an energy source configured to illuminate an area being monitored for release of a gas.

15. The system of claim 12 wherein the alert includes an image that shows the release of the gas and the monitored area.

16. The system of claim 12 wherein the alert includes a type of gas which is leaking.

Assignments (2)
CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNEE NAME PREVIOUSLY RECORDED ON REEL 61289 FRAME 707. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded May 9, 2025
From: O'NEILL, MARY D.; MOREY, MARK; TERRY, DAVID
To: MISSION SUPPORT AND TEST SERVICES, LLC
Reel/Frame 071247/0432 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 19, 2022
From: O'NEILL, MARY D.; MOREY, MARK; TERRY, DAVID
To: MISSION SUPPORT AND TESTS SERVICES, LLC
Reel/Frame 061289/0707 →
Continuity (3)
Continuation 16989732 · Aug 10, 2020
Provisional Application 62885144 · Aug 9, 2019
Related Publication 20220337788A1 · Oct 20, 2022
References Cited (62)
US 2562181A · Frommer · 1951 [cited by applicant]
US 2737591A · Wright et al. · 1956 [cited by applicant]
US 4598247A · Mako · 1986 [cited by applicant]
US 5200149A · Firsher · 1993 [cited by applicant]
US 5303024A · Thierman · 1994 [cited by applicant]
US 6025788A · Diduck · 2000 [cited by applicant]
US 7358860B2 · Germouni · 2008 [cited by applicant]
US 7375814B2 · Reichardt · 2008 [cited by applicant]
US 7685873B2 · Shapira · 2010 [cited by applicant]
US 10466174B2 · Glacer · 2019 [cited by applicant]
US 20080048121A1 · Hinnrichs · 2008 [cited by applicant]
US 20080213442A1 · Hughes · 2008 [cited by applicant]
US 20110013016A1 · Tillotson · 2011 [cited by applicant]
US 20140125860A1 · Tofsted · 2014 [cited by applicant]
US 20150254813A1 · Foi · 2015 [cited by applicant]
US 20160005154A1 · Meyers · 2016 [cited by applicant]
US 20170006227A1 · O'Neill · 2017 [cited by applicant]
US 20190346337A1 · Grimberg · 2019 [cited by applicant]
Ghazali, M.F., 2012. Leak detection using instantaneous frequency analysis (Doctoral dissertation, University of Sheffield). (Year: 2012). [cited by examiner]
Kaewwaewnoi W, Prateepasen A, Kaewtrakulpong P. Investigation of relationship between internal fluid leakage through a valve and the acoustic emission generated from the leakage. Measurement. Feb. 1, 2010;43(2):274-82. … [cited by examiner]
Daneti M. On using a simplified model for leak detection improving in fluid filled pipelines. In2010 IEEE 15th Conference on Emerging Technologies & Factory Automation (ETFA 2010) Sep. 13, 2010 (pp. 1-8). IEEE. (Year: 2… [cited by examiner]
Sydney Goldstein, “Fluid Mechanics in the First Half of this Centruy”, Harvard University, Cambridge, Massachusetts, Annu. Rev. Fluid Mech. 1969.1:1-29, downloaded from www.annualreviews.org, 29 pages, date unknown. [cited by applicant]
David L. Fried, “Probability of getting a lucky short-exposure emage through turbulence*”, The Optical Sciences Company, (c) Optical Society of America, vol. 68, No. 12, Dec. 12, 1978. [cited by applicant]
A. N. Kolmogorov, “The Local Structure of Turbulence in Incompressible Viscous Fluid for Very Large Reynolds Numbers”, Proceeding: Mathematical and Physical Sciences, vol. 434 No. 1890, Turbulence and Stochastic Process… [cited by applicant]
Jacques M. Beckers, “Adaptive Optics for Astronomy: Principles, Performance, and Applications”, Annual Reviews, www.annualreviews.org/aronline, Annu. Rev. Aston. Astrophys. 1993, 31: 13-62, Copyright © 1993 by Annual Re… [cited by applicant]
M. S. Belen'kii, et al., “Experimental validation of the differential image motion lidar concept”, Optic Letters, vol. 25, No. 8, © 2000 Optical Society of America, Apr. 15, 2000, 3 pages. [cited by applicant]
David H. Tofsted, Army Research Laboratory, ARL, “Turbulence Simulation: Outer Scale Effects on the Refractive Index Spectrum” Computational and Informational Sciences Directorate Battlefield Environment Division, ARL-T… [cited by applicant]
Mikhail S. Belen'kii, et al., “Turbulence-induced edge image waviness: theory and experiment”, © 2001 Optical Society of America, Applied Optics vol. 40, No. 9, Mar. 20, 2001, 8 pages. [cited by applicant]
S.Zamek, et al., “Turbulence strength estimation from an abritratry set of atmospherically degraded images”, Department of Electo-Optics Engineering, Ben Gurion University, © 2006 Optical Society of America, vol. 23, No… [cited by applicant]
Patrice Martinez, et al., “On the Difference between Seeing and Image Quality: When the Turbulence Outer Scale Enters the Game”, Telescopes and Instrumentation, The Messenger 141, Sep. 2010, 4 pages. [cited by applicant]
David H. Tofsted, “Reanalysis of turbulence effects on short-exposure passive imaging”, Optical Engineering, vol. 50(1), 016001, Jan. 2011, 9 pages. [cited by applicant]
Terry, et al. “(U) Real-Time Atmospheric Mitigation Sensor System (RAMS [cited by applicant]
Ingmar G.E. Renhorn, et al., “Experimental observation of spatial correlation of scintillations from an extended incoherent source”, Optical Engineering, SPIE DigitalLibrary.org/oe, vol. 52(2), 026001, Feb. 2013, 7 page… [cited by applicant]
David H. Tofsted, “Extended high-angular-frequency analysis of turbulence effects on short-exposure imaging”, Optical Engineering, SPIE Digital Library.org/oe, vol. 53(4), Apr. 2014, 11 pages. [cited by applicant]
“Measurements of the Refractive Index Structure Constant and Studies on Beam wander”, Keiichi Yano, et al., Proc. International Conference on Space Optical Systems and Applications (ICSOS) 2014, P15, Kobe, Japan, May 7-… [cited by applicant]
P. A. Konyaev, et al., “Passive Optical Methods in Measurement of the Structure Parameter of the Air Refractive Index—Inverse Problems of Atmospheric and Ocean Optics”, ISSN 1024-8560, Atmospheric and Oceanic Optics, 20… [cited by applicant]
Scintillometer, Wikipedia, https://en.wikipedia.org/wiki/Scintillometer, May 24, 2016, 2 pages. [cited by applicant]
“Refractive Index”, Wikipedia, https://en.wikipedia.org/wiki/Refractive_index, May 24, 2016, 17 pages. [cited by applicant]
M.G. Sterenborg, et al., “Determining the refractive index structure constant using high-resolution radiosonde data”, paper, 40 pages, date unknown. [cited by applicant]
Richard L. Espinola, et al., “Turbulence degradation and mitigation performance for handheld weapon ID”, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII, edited by Gerald C. Holst, Keith A. Krape… [cited by applicant]
Born, M., E. Wolf, Principles of Optics, Section 9.2, Pergamon, New York, 1965. [cited by applicant]
Carrano, C., “Bispectral speckle imaging algorithm performance of specific simulated scenarios,” LLNLTR645877, Lawrence Livermore National Laboratory, Livermore, California, Nov. 2013. [cited by applicant]
Fried, D. L., “Optical heterodyne detection of an atmospherically distorted signal wavefront,” Proc. IEEE 55 (1967) 57-77. [cited by applicant]
Hufnagel, R. E., “The probability of a lucky exposure,” Tech. Memo. REH0155, Perkin-Elmer, Norwalk, Connecticut, Feb. 1989. [cited by applicant]
Lukin, V. P., N. N. Botygina, O.N. Emaleev, P.A. Konyaev, “Wavefront sensors for adaptive optical systems,” Meas. Sci. Rev. 10, 3 (2010) 102-107. [cited by applicant]
Max, C., Lecture 3 of Astronomy 289C: Adaptive Optics and its Applications, University of California, Santa Cruz, Apr. 8, 2010. [cited by applicant]
Noll, R. J., “Zernike polynomials and atmospheric turbulence,” J. Opt. Soc. Am. 66 (1976) 207-211. [cited by applicant]
O'Neill, M. D., D. Terry, A. Potter, I. McKenna, “Passive method to characterize atmospheric turbulence,” in Site-Directed Research and Development, FY 2013, National Security Technologies, LLC, Las Vegas, Nevada, 2014,… [cited by applicant]
Rais, M., J.-M. Morel, C. Thiebaut, J.-M. Delvit, G. Facciolo, “Improving the accuracy of a Shack-Hartmann wavefront sensor on extended scenes,” 6th International Workshop on New Computational Methods for Inverse Proble… [cited by applicant]
Sidick E., J. J. Green, C. M. Ohara, D. C. Redding, “An adaptive cross-correlation algorithm for extended scene Shack-Hartmann wavefront sensing,” in Adaptive Optics: Analysis and Methods/Computational Optical Sensing a… [cited by applicant]
Sidick, E., “Extended scene Shack-Hartmann wavefront sensor algorithm: Minimization of scene content dependent shift estimation errors,” Appl. Opt. 52 (2013) 6487-6496. [cited by applicant]
Tofsted, D., “Passive adaptive imaging through turbulence,” Proc. SPIE 9833 (2016) 98330B. [cited by applicant]
O'Neill, M. D., D. Terry, “Portable COTS RGB wavefront sensor,” MSS Passive Sensors, Gaithersburg, Maryland, 2016. [cited by applicant]
O'Neill, M. D., D. Terry, “RGB wavefront sensor for turbulence mitigation,” in Site-Directed Research and Development, FY 2016, National Security Technologies, LLC, Las Vegas, Nevada, 2017, 09-117. [cited by applicant]
Poyneer, L. A., K. La Fortune, C. Chan, “Scene-based wave-front sensing for remote imaging,” UCRL-JC-154654 Lawrence Livermore National Laboratory, Livermore, California, 2003. [cited by applicant]
Vdovin, G., O. Soloviev, M. Loktev, V. Patlan, “OKO Guide to Adaptive Optics,” 4th edition, OKO Technologies, May 2013. [cited by applicant]
Hufnager, R.E., Measurement of Atmospheric Turbulence via Observations of Instantaneous Optical Blur Functions, Perkin-Elmer Corporation, Norwalk, Connecticut, USA, 1970, 11 pages. [cited by applicant]
Sam McDonald. “New Tool for Measuring Carbon Dioxide in the Atmosphere Shows Promise”, NASA Langley Research Center. NASA TV. National Aeronautics and Space Administration. https://www.nasa.gov/larc/new-tool-for-measuri… [cited by applicant]
“What is a blackbody source and what is it used for? (FAQ—Thermal)” FAQs, National Physical Laboratory, http://www.npl.co.uk/reference/faqs/what-is-a-blackbody-source-and-what-is-it-used-for-(faq-thermal), Oct. 8, 2007 … [cited by applicant]
Liu, et al., “Image Processing Algorithms for Crack Detection in Welded Structures via Pulsed Eddy Current Thermal Imaging”, IEEE Instrumentation & Measurement Magazine, Aug. 10, 2017; 20(4):34-44. [cited by applicant]
Terry, et al. “Passive Method to Characterize Atmospheric Turbulence”, Nevada Test Site/National Security Technologies, LLC (United States); Sep. 9, 2014. (Year: 2014). [cited by applicant]
O'Neill, et al., “Strength of Turbulence from Video Imagery”, Nevada National Security Site/Mission Support and Test Services LLC; Las Vegas, NV (United States); Jan. 1, 2017. (Year: 2017). [cited by applicant]