公开(公告)号：US11488294B2

公开(公告)日：2022-11-01

申请号：US16939277

申请日：2020-07-27

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

Inventor： Yawei Wen ,  Jiabing Leng ,  Minghao Liu ,  Yulin Xu ,  Jiangliang Guo ,  Xu Li

IPC: G02F1/13 ,  G06N3/08 ,  G06T7/00

Abstract: Provided are a method for detecting display screen quality, an apparatus, an electronic device and a storage medium. The method includes: receiving a quality detection request sent by a console deployed on a display screen production line, the quality detection request including a display screen image collected by an image collecting device on the display screen production line; inputting the display screen image into a defect detection model to obtain a defect detection result, the defect detection model being obtained by training historical defective display screen images using a structure of deep convolutional neural networks and an object detection algorithm; and determining, according to the defect detection result, a defect on a display screen corresponding to the display screen image, a defect category corresponding to the defect, and a position corresponding to the defect.

公开(公告)号：US11380232B2

公开(公告)日：2022-07-05

申请号：US16936806

申请日：2020-07-23

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

Inventor： Yawei Wen ,  Jiabing Leng ,  Minghao Liu ,  Yulin Xu ,  Jiangliang Guo ,  Xu Li

IPC: G09G3/00 ,  G01N21/956

Abstract: A display screen quality detection method, an apparatus, an electronic device and a storage medium. The method includes receiving a quality detection request sent by a console deployed on a display screen production line, where the quality detection request includes a display screen image captured by an image capturing device on the display screen production line, performing image preprocessing on the display screen image, and inputting the preprocessed display screen image into a defect detection model to obtain a defect detection result, where the defect detection model is obtained by training with a historical defect display screen image using a deep convolutional neural network structure and an instance segmentation algorithm, determining, according to the defect detection result, quality of a display screen corresponding to the display screen image. The technical solution has high defect detection accuracy, good system performance, and high business expansion capability.