The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 19, 2020

Filed:

May. 23, 2018
Applicant:

Southwest Research Institute, San Antonio, TX (US);

Inventors:

Maria S. Araujo, San Antonio, TX (US);

Samantha G. Blaisdell, San Antonio, TX (US);

Daniel S. Davila, San Antonio, TX (US);

Edmond M. DuPont, San Antonio, TX (US);

Sue A. Baldor, San Antonio, TX (US);

Shane P. Siebenaler, San Antonio, TX (US);

Assignee:

Southwest Research Institute, San Antonio, TX (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06N 3/04 (2006.01); F17D 5/06 (2006.01); G06N 5/04 (2006.01); G06K 9/62 (2006.01); G06T 5/50 (2006.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01); G06K 9/00 (2006.01); G06K 9/46 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); F17D 5/06 (2013.01); G06K 9/00637 (2013.01); G06K 9/4628 (2013.01); G06K 9/6267 (2013.01); G06K 9/6274 (2013.01); G06K 9/6289 (2013.01); G06N 3/0454 (2013.01); G06N 3/0481 (2013.01); G06N 5/046 (2013.01); G06N 20/00 (2019.01); G06T 5/50 (2013.01); G06T 7/0004 (2013.01); G06K 2009/00644 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/10048 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01);
Abstract

A method of training a learning machine to detect spills of hydrocarbon liquids from pipelines. A neural network is trained by collecting samples of a number of different ground materials as well as a number of liquid hydrocarbons. For each hydrocarbon, a spill is simulated on each ground material. For each of these spills, a thermal camera and a visible light camera are used to capture images. The images from the two cameras are fused, and input to the neural network for classification training. Once the neural network is trained, a system having the two cameras and the neural network can be used to detect actual hydrocarbon spills.


Find Patent Forward Citations

Araujo, Maria. (2020). Detection of hazardous leaks from pipelines using optical imaging and neural network (U.S. Patent No. 10657443). U.S. Patent and Trademark Office. https://idiyas.com/patent/badge/10657443

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

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