公开(公告)号：US20210365738A1

公开(公告)日：2021-11-25

申请号：US17444427

申请日：2021-08-04

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

Inventor： Zhuang Jia ,  Xiang Long ,  Honghui Zheng ,  Yan Peng ,  Yuan Feng ,  Bin Zhang ,  Xiaodi Wang ,  Pengcheng Yuan ,  Ying Xin ,  Shumin Han

IPC: G06K9/62 ,  G06K9/00

Abstract: The present disclosure discloses a method and apparatus for training a model, a method and apparatus for predicting a mineral, a device and a storage medium, and relates to the fields of computer vision and deep learning technologies. An implementation of the method may include: acquiring a target hyperspectral image of a target area, the target hyperspectral image including at least one pixel point annotated with a mineral category; determining a mask image corresponding to the target hyperspectral image; determining a sample hyperspectral image according to the target hyperspectral image and the mask image; determining an annotation vector of each pixel point according to the at least one pixel point annotated with the mineral category; and training a model according to the sample hyperspectral image and the annotation vector of the each pixel point.

公开(公告)号：US20210390728A1

公开(公告)日：2021-12-16

申请号：US17412574

申请日：2021-08-26

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

Inventor： Yan PENG ,  Xiang Long ,  Shumin Han ,  Honghui Zheng ,  Zhuang Jia ,  Xiaodi Wang ,  Pengcheng Yuan ,  Yuan Feng ,  Bin Zhang ,  Ying Xin

IPC: G06T7/62 ,  G06K9/32 ,  G06K9/52 ,  G06K9/62

Abstract: An object area measurement method and an apparatus are provided, relating to the computer vision and deep learning technology. The method includes acquiring an original image with a spatial resolution, the original image including a target object; acquiring an object identification model including at least two sets of classification models; generating one or more original image blocks based on the original image; performing operations on each original image block: scaling each original image block at at least two scaling levels to obtain scaled image blocks with at least two sizes, the scaled image blocks respectively corresponding to the at least two sets of classification models, and inputting the scaled image blocks into the object identification model to obtain an identification result of the target object; and determining an area of the target object based on the respective identification results of the one or more original image blocks and the spatial resolution.

公开(公告)号：US11921276B2

公开(公告)日：2024-03-05

申请号：US17379428

申请日：2021-07-19

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

Inventor： Xiang Long ,  Yan Peng ,  Shufei Lin ,  Ying Xin ,  Bin Zhang ,  Pengcheng Yuan ,  Xiaodi Wang ,  Yuan Feng ,  Shumin Han

IPC: G02B21/24 ,  G02B21/36 ,  G06F18/213 ,  G06F18/214

CPC classification number: G02B21/244 ,  G02B21/367 ,  G06F18/213 ,  G06F18/214

Abstract: Provided are a method and apparatus for evaluating image relative definition, a device and a medium, relating to technologies such as computer vision, deep learning and intelligent medical. A specific implementation solution is: extracting a multi-scale feature of each image in an image set, where the multi-scale feature is used for representing definition features of objects having different sizes in an image; and scoring relative definition of each image in the image set according to the multi-scale feature by using a relative definition scoring model pre-trained, where the purpose for training the relative definition scoring model is to learn a feature related to image definition in the multi-scale feature.

公开(公告)号：US11669990B2

公开(公告)日：2023-06-06

申请号：US17412574

申请日：2021-08-26

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

Inventor： Yan Peng ,  Xiang Long ,  Shumin Han ,  Honghui Zheng ,  Zhuang Jia ,  Xiaodi Wang ,  Pengcheng Yuan ,  Yuan Feng ,  Bin Zhang ,  Ying Xin

IPC: G06T7/62 ,  G06F18/241 ,  G06F18/25 ,  G06F18/2137 ,  G06V10/764 ,  G06V10/80 ,  G06V10/82 ,  G06V10/32 ,  G06V10/50 ,  G06V10/26 ,  G06V20/13

CPC classification number: G06T7/62 ,  G06F18/2137 ,  G06F18/241 ,  G06F18/253 ,  G06V10/26 ,  G06V10/32 ,  G06V10/50 ,  G06V10/764 ,  G06V10/809 ,  G06V10/82 ,  G06V20/13 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: An object area measurement method and an apparatus are provided, relating to the computer vision and deep learning technology. The method includes acquiring an original image with a spatial resolution, the original image including a target object; acquiring an object identification model including at least two sets of classification models; generating one or more original image blocks based on the original image; performing operations on each original image block: scaling each original image block at at least two scaling levels to obtain scaled image blocks with at least two sizes, the scaled image blocks respectively corresponding to the at least two sets of classification models, and inputting the scaled image blocks into the object identification model to obtain an identification result of the target object; and determining an area of the target object based on the respective identification results of the one or more original image blocks and the spatial resolution.

公开(公告)号：US11763552B2

公开(公告)日：2023-09-19

申请号：US17116597

申请日：2020-12-09

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

Inventor： Shufei Lin ,  Jianfeng Zhu ,  Pengcheng Yuan ,  Bin Zhang ,  Shumin Han ,  Yingbo Xu ,  Yuan Feng ,  Ying Xin ,  Xiaodi Wang ,  Jingwei Liu ,  Shilei Wen ,  Hongwu Zhang ,  Errui Ding

IPC: G06V10/82 ,  G06N3/088 ,  G06T7/00 ,  G06F18/214 ,  G06N3/045 ,  G06V10/776 ,  G06V20/60

CPC classification number: G06V10/82 ,  G06F18/2148 ,  G06N3/045 ,  G06N3/088 ,  G06T7/0004 ,  G06V10/776 ,  G06V20/60 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30124

Abstract: A method for detecting a surface defect, a method for training model, an apparatus, a device, and a medium, are provided. The method includes: inputting a surface image of the article for detection into a defect detection model to perform a defect detection, and acquiring a defect detection result output by the defect detection model; inputting a surface image of a defective article determined to be defective into an image discrimination model based on the defect detection result to determine whether the surface image of the defective article is defective, wherein the image discrimination model is a trained generative adversarial networks model, and the generative adversarial networks model is obtained by training using a surface image of a defect-free good article; and adjusting the defect detection result of the surface image of the defective article according to a determination result of the image discrimination model.

公开(公告)号：US20210383520A1

公开(公告)日：2021-12-09

申请号：US17412435

申请日：2021-08-26

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

Inventor： Mingyuan Mao ,  Yuan Feng ,  Ying Xin ,  Pengcheng Yuan ,  Bin Zhang ,  Xiaodi Wang ,  Xiang Long ,  Yan Peng ,  Honghui Zheng ,  Shumin Han

IPC: G06T5/50 ,  G06T7/194 ,  G06T3/00 ,  G06T3/40 ,  G06K9/62

Abstract: The present disclosure discloses a method and apparatus for generating an image, a device, a storage medium and a program product, relates to the field of artificial intelligence, and particularly to computer vision and deep learning technologies, and may be applied in smart cloud and power grid inspection scenarios. A particular implementation of the method comprises: acquiring an original insulator image; performing an image transformation on the original insulator image to obtain a composite insulator image; and inputting the original insulator image and the composite insulator image into a pre-trained generative adversarial network to generate a target insulator image. According to the implementation, the image transformation is performed on the original insulator image, and then, massive target insulator images are generated through the generative adversarial network.