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:
Feb. 26, 2019

Filed:

Aug. 22, 2017
Applicant:

Northrop Grumman Systems Corporation, Falls Church, VA (US);

Inventors:

Victor Y. Wang, San Diego, CA (US);

Kevin A. Calcote, La Mesa, CA (US);

Assignee:

Northrop Grumman Systems Corporation, Falls Church, VA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06N 3/00 (2006.01); G06T 7/20 (2017.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06K 9/6257 (2013.01); G06K 9/00744 (2013.01); G06K 9/6267 (2013.01); G06N 3/0445 (2013.01); G06N 3/08 (2013.01); G06T 7/20 (2013.01); G06K 9/6263 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A system for distributive training and weight distribution in a neural network. The system includes a training facility having a training neural network that detects and classifies objects in training images so as to train weights of nodes in the training neural network, and a plurality of object detection and classification units each including an image source that provides image frames, and at least one classification and prediction neural network that identifies, classifies and indicates relative velocity of objects in the image frames. Each unit transmits its image frames to the training facility so that the training neural network further trains the weights of the nodes in the training neural network, and the trained neural network weights are transmitted from the training facility to each of the object detection and classification units so as to train weights of nodes in the at least one classification and prediction neural network.


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