公开(公告)号：US12154262B2

公开(公告)日：2024-11-26

申请号：US17444883

申请日：2021-08-11

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

Inventor： Huihui Xiao ,  Lei Nie ,  Jianfa Zou ,  Feng Huang

IPC: G06T7/00 ,  G06F18/22 ,  G06N20/00 ,  G06T5/00 ,  G06T5/50

Abstract: Provided are an image defect detection method and apparatus, an electronic device, a storage medium and a product. The method includes acquiring a to-be-detected image; obtaining a restored image corresponding to the to-be-detected image based on the to-be-detected image, at least one mask image group and a plurality of defect-free positive sample images, where each mask image group includes at least two binary images having a complementary relationship, and different mask image groups have different image sizes; and locating a defect of the to-be-detected image based on the to-be-detected image and each restored image. The solution solves the problem in which a related defect detection method requires numerous manual operations and has a low detection accuracy due to subjective factors of a worker.

公开(公告)号：US11615524B2

公开(公告)日：2023-03-28

申请号：US17248053

申请日：2021-01-07

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

Inventor： Ye Su ,  Lei Nie ,  Jianfa Zou ,  Feng Huang

IPC: G06K9/00 ,  G06T7/00 ,  G06V10/44 ,  G06V10/48 ,  G06V20/64

Abstract: A product defect detection method and apparatus, an electronic device, and a storage medium are provided. A method includes: acquiring a multi-channel image of a target product; inputting the multi-channel image to a defect detection model, wherein the defect detection model includes a plurality of convolutional branches, a merging module and a convolutional headbranch; performing feature extraction on each channel in the multi-channel image by using the plurality of convolutional branches, to obtain a plurality of first characteristic information; merging the plurality of first characteristic information by using the merging module, to obtain second characteristic information; performing feature extraction on the second characteristic information by using the convolutional headbranch, to obtain third characteristic information to be output by the defect detection model; and determining defect information of the target product based on the third characteristic information.