公开(公告)号：US11741726B2

公开(公告)日：2023-08-29

申请号：US17494682

申请日：2021-10-05

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

Inventor： Yingying Li ,  Xiao Tan ,  Hao Sun

IPC: G06V20/56 ,  G06T7/194 ,  G06T7/70 ,  G06V20/54 ,  G06V10/22 ,  G08G1/01 ,  G06F18/23 ,  G06F18/2431 ,  G06F18/2413

CPC classification number: G06V20/588 ,  G06F18/23 ,  G06F18/2431 ,  G06F18/24137 ,  G06T7/194 ,  G06T7/70 ,  G06V10/22 ,  G06V20/54 ,  G08G1/0133 ,  G06T2207/30256

Abstract: A lane line detection method, an electronic device, and a storage medium, related to the field of artificial intelligence, and particularly related to computer vision and deep learning technologies, which can be applied to intelligent traffic scenes, are provided. The method includes: dividing an image into a foreground region and a background region; determining a solid line and a dotted line included in the foreground region; determining, according to the solid line and the dotted line comprised in the foreground region, whether a dotted-and-solid line is included in the foreground region; and determining a lane line detection result according to the solid line, the dotted line, and whether a dotted-and-solid line is comprised in the foreground region. According to the technical solution, the accuracy of lane line detection can be improved.

公开(公告)号：US20230068238A1

公开(公告)日：2023-03-02

申请号：US18049326

申请日：2022-10-25

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

Inventor： Yingying Li ,  Xiao Tan ,  Hao Sun

IPC: G06V10/44 ,  G06T7/50 ,  G06T7/10

Abstract: A method for processing an image includes obtaining an image to be processed; obtaining a depth feature map by inputting the image to be processed into a depth feature extraction network in an image recognition model, and obtaining a semantic segmentation feature map by inputting the image to be processed into a semantic feature extraction network of the image recognition model; obtaining a target depth feature map fused with semantic features and a target semantic segmentation feature map fused with depth features by inputting the depth feature map and the semantic segmentation feature map into a feature interaction network of the recognition model for fusion; and obtaining a depth estimation result and a semantic segmentation result by inputting the target depth feature map and the target semantic segmentation feature map into a corresponding output network in the recognition model.

公开(公告)号：US20230245429A1

公开(公告)日：2023-08-03

申请号：US18003463

申请日：2022-01-29

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

Inventor： Yue He ,  Yingying Li ,  Xiao Tan ,  Hao Sun

IPC: G06V10/774 ,  G06V20/56

CPC classification number: G06V10/774 ,  G06V20/588

Abstract: A method for training a lane line detection model includes: obtaining a plurality of road condition sample images and a plurality pieces of labeled lane line information corresponding to the plurality of road condition sample images; determining a plurality of elements corresponding to the plurality of road condition sample images and a plurality of element semantics corresponding to the plurality of elements; and obtaining the lane line detection model by training an initial artificial intelligence model based on the plurality of road condition sample images, the plurality of elements, the plurality of element semantics and the plurality pieces of labeled lane line information.