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:
Jan. 21, 2025

Filed:

Nov. 30, 2021
Applicant:

George Mason University, Fairfax, VA (US);

Inventors:

Yongxin Wang, Fairfax, VA (US);

Duminda Wijesekera, McLean, VA (US);

Assignee:

George Mason University, Fairfax, VA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/56 (2022.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01);
U.S. Cl.
CPC ...
G06V 10/56 (2022.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06V 10/806 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01);
Abstract

A computer-implemented method of detecting one or more objects in a driving environment located externally to a vehicle, and a vehicle imaging system configured to detect one or more objects. The computer-implemented method includes training a first neural network to detect objects in a color video stream, the first neural network having a plurality of mid-level color features at a plurality of scales, and training a second neural network, operatively coupled to color neural network and an infrared video stream, to match, at the plurality of scales, mid-level infrared features of the second neural network to mid-level color features of the first neural network. A pixel-level invisibility map is then generated from the color video stream and the infrared video stream by determining differences, at each of the plurality of scales, between mid-level color features at the first neural network and mid-level infrared features at the second infrared neural network, and coupling the result to a fusing function.


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