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:
Oct. 19, 2021

Filed:

Sep. 18, 2019
Applicants:

Wipro Limited, Bangalore, IN;

District of Columbia Water and Sewer Authority, Washington, DC (US);

Inventors:

Rupesh Wasudevrao Kumbhare, Bangalore, IN;

Deepak Dinkar, Bangalore, IN;

Saravanan Solaiyappan, Chennai, IN;

Douglas Adams, E Logan, UT (US);

Thomas L. Kuczynski, Fulton, MD (US);

Hari Kurup, Falls Church, VA (US);

Nichol Sowell, Upper Marlboro, MD (US);

Chad Rogers, Alexandria, VA (US);

LaShema M. Burrell, Temple Hills, MD (US);

Assignees:

Wipro Limited, Bangalore, IN;

District of Columbia Water & Sewer Authority, Washington, DC (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/00 (2017.01); G06T 7/269 (2017.01); G06F 16/55 (2019.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06T 7/001 (2013.01); G06F 16/55 (2019.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/269 (2017.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30108 (2013.01);
Abstract

A method and a system are described for detection of anomalies in surfaces, such as pipes. The method includes receiving an input comprising surface material type and plurality of frames of a real-time video stream associated with the surface. The method includes eliminating unwanted frames based on magnitude of 2-Dimensional optical flow vectors of the plurality of frames. The method includes identifying potential anomaly frames based on contours in a dense map created using a magnitude of displacement of each pixel of a frame. The method includes detecting in real-time anomalies in anomaly frames from potential anomaly frames based on trained models selected from a Model Mapping Table. The method includes classifying anomalies in the anomaly frames into anomaly classes using one or more deep learning techniques. The method includes generating a health report comprising anomalies in anomaly frames associated with the surface and providing health report to a user.


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