公开(公告)号：US20210232856A1

公开(公告)日：2021-07-29

申请号：US17213746

申请日：2021-03-26

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Yingying Li ,  Xiao Tan ,  Minyue Jiang ,  Hao Sun

IPC: G06K9/62 ,  G06T3/00 ,  G06N3/08

Abstract: The present disclosure provides an image processing method. An image to be classified is input into a feature extraction model to generate N dimensional features. Dimension fusion is performed on M features of the N dimensional features to obtain M dimension fusion features. The image to be classified is processed based on M dimension fusion features and remaining features of the N dimensional features other than the M features.

公开(公告)号：US11727676B2

公开(公告)日：2023-08-15

申请号：US17213746

申请日：2021-03-26

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Yingying Li ,  Xiao Tan ,  Minyue Jiang ,  Hao Sun

IPC: G06V10/82 ,  G06T3/00 ,  G06V10/77 ,  G06V10/80 ,  G06F18/213 ,  G06F18/211 ,  G06F18/25

CPC classification number: G06V10/82 ,  G06F18/211 ,  G06F18/213 ,  G06F18/253 ,  G06T3/00 ,  G06V10/7715 ,  G06V10/806

Abstract: The present disclosure provides an image processing method. An image to be classified is input into a feature extraction model to generate N dimensional features. Dimension fusion is performed on M features of the N dimensional features to obtain M dimension fusion features. The image to be classified is processed based on M dimension fusion features and remaining features of the N dimensional features other than the M features.

公开(公告)号：US20220270373A1

公开(公告)日：2022-08-25

申请号：US17743410

申请日：2022-05-12

Applicant: Beijing Baidu Netcom Science and Technology Co., Ltd.

Inventor： Xipeng Yang ,  Minyue Jiang ,  Xiao Tan ,  Hao Sun ,  Shilei Wen ,  Hongwu Zhang ,  Errui Ding

IPC: G06V20/52 ,  G06V10/40 ,  G06V10/764 ,  G06V10/774 ,  G06V10/22

Abstract: A method, an electronic device and a storage medium are provided. The method may include: acquiring a to-be-inspected image; inputting the to-be-inspected image into a pre-established vehicle detection model to obtain a vehicle detection result, where the vehicle detection result includes category information, coordinate information, coordinate reliabilities, and coordinate error information of detection boxes, and the vehicle detection model is configured for characterizing a corresponding relationship between images and vehicle detection results; selecting, based on the coordinate reliabilities of the detection boxes, a detection box from the vehicle detection result for use as a to-be-processed detection box; and generating, based on coordinate information and coordinate error information of the to-be-processed detection box, coordinate information of a processed detection box.