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:
Nov. 22, 2022

Filed:

Jul. 20, 2021
Applicant:

Electronics and Telecommunications Research Institute, Daejeon, KR;

Inventors:

Dong-Jin Lee, Daejeon, KR;

Do-Wook Kang, Seoul, KR;

Jungyu Kang, Daejeon, KR;

Joo-Young Kim, Daejeon, KR;

Kyoung-Wook Min, Sejong-si, KR;

Jae-Hyuck Park, Daejeon, KR;

Kyung-Bok Sung, Daejeon, KR;

Yoo-Seung Song, Daejeon, KR;

Taeg-Hyun An, Daejeon, KR;

Yong-Woo Jo, Daejeon, KR;

Doo-Seop Choi, Sejong-si, KR;

Jeong-Dan Choi, Daejeon, KR;

Seung-Jun Han, Daejeon, KR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06T 7/80 (2017.01); G06V 10/24 (2022.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
G06K 9/6265 (2013.01); G06K 9/628 (2013.01); G06K 9/6257 (2013.01); G06K 9/6277 (2013.01); G06K 9/6288 (2013.01); G06T 7/80 (2017.01); G06V 10/245 (2022.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01); G06V 20/647 (2022.01); G06T 2207/10028 (2013.01); G06T 2207/30252 (2013.01);
Abstract

Disclosed herein are an object recognition apparatus of an automated driving system using error removal based on object classification and a method using the same. The object recognition method is configured to train a multi-object classification model based on deep learning using training data including a data set corresponding to a noise class, into which a false-positive object is classified, among classes classified by the types of objects, to acquire a point cloud and image data respectively using a LiDAR sensor and a camera provided in an autonomous vehicle, to extract a crop image, corresponding to at least one object recognized based on the point cloud, from the image data and input the same to the multi-object classification model, and to remove a false-positive object classified into the noise class, among the at least one object, by the multi-object classification model.


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