METHOD AND APPARATUS FOR DETECTING TABLE, DEVICE AND STORAGE MEDIUM

    公开(公告)号:US20220277575A1

    公开(公告)日:2022-09-01

    申请号:US17743687

    申请日:2022-05-13

    Abstract: A method and apparatus for detecting a table. The method includes: acquiring a to-be-processed image; inputting the to-be-processed image into a pre-trained deep learning model, and outputting a full table detection branch result, a column detection branch result and a header detection branch result through the deep learning model; where the full table detection branch result represents a detection result for a full table in the to-be-processed image, the column detection branch result represents a detection result for a column in the table in the to-be-processed image, and the header detection branch result represents a detection result for a header in the to-be-processed image; and obtaining a detection result of the table in the to-be-processed image, based on the full table detection branch result, the column detection branch result and the header detection branch result.

    Method and apparatus for detecting table, device and storage medium

    公开(公告)号:US12154359B2

    公开(公告)日:2024-11-26

    申请号:US17743687

    申请日:2022-05-13

    Abstract: A method and apparatus for detecting a table. The method includes: acquiring a to-be-processed image; inputting the to-be-processed image into a pre-trained deep learning model, and outputting a full table detection branch result, a column detection branch result and a header detection branch result through the deep learning model; where the full table detection branch result represents a detection result for a full table in the to-be-processed image, the column detection branch result represents a detection result for a column in the table in the to-be-processed image, and the header detection branch result represents a detection result for a header in the to-be-processed image; and obtaining a detection result of the table in the to-be-processed image, based on the full table detection branch result, the column detection branch result and the header detection branch result.

    Method for detecting display screen quality, apparatus, electronic device and storage medium

    公开(公告)号:US11488294B2

    公开(公告)日:2022-11-01

    申请号:US16939277

    申请日:2020-07-27

    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.

    Display screen quality detection method, apparatus, electronic device and storage medium

    公开(公告)号:US11380232B2

    公开(公告)日:2022-07-05

    申请号:US16936806

    申请日:2020-07-23

    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.

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