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Paper defect detection method and device

A defect detection and paper technology, applied in the field of machine learning, can solve problems such as low efficiency, high labor cost, slow speed, etc., and achieve the effect of low labor cost, high detection efficiency, and high detection accuracy.

Inactive Publication Date: 2020-12-11
深兰智能科技(上海)有限公司
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Problems solved by technology

[0003] At present, the detection of paper defects is mostly done through manual visual observation, which is slow, inefficient, and labor-intensive.

Method used

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  • Paper defect detection method and device
  • Paper defect detection method and device

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Embodiment Construction

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0022] Such as figure 1 As shown, the paper defect detection method in the embodiment of the present invention includes the following steps:

[0023] S1. Acquire a sample data set, where the sample data set includes multiple sample product images with paper defects and multiple sample product images without paper defects.

[0024] In one embodiment of the present invention, a camera can be used to take photos of a large number of sample products, for example,...

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Abstract

The invention provides a paper defect detection method and device. The method comprises the steps of obtaining a sample data set which comprises a plurality of sample product images with paper defectsand a plurality of sample product images without paper defects; connecting adjacent attention modules in a convolutional neural network introduced with a self-attention mechanism to construct a deepconnection attention network; training the convolutional neural network containing the deep connection attention network through the sample data set to obtain a paper defect detection model; acquiringan image of a to-be-detected product; and inputting the image of the to-be-detected product into the paper defect detection model to judge whether paper defects exist or not. The method is relativelyhigh in detection efficiency, relatively low in labor cost and relatively high in detection accuracy.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a paper defect detection method, a paper defect detection device, a computer device, a non-transitory computer-readable storage medium and a computer program product. Background technique [0002] After the paper is produced, there may be some defects, such as scratches, clear spots, breakage, black spots, holes, wrinkles, oil spots, etc., so it is necessary to carry out defect detection before it is put into the market or further processed. [0003] At present, the detection of paper defects is mostly done through manual visual observation, which is slow, inefficient, and labor-intensive. Contents of the invention [0004] In order to solve the above technical problems, the present invention provides a paper defect detection method and device, which have high detection efficiency, low labor cost and high detection accuracy. [0005] The technical scheme that ...

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Application Information

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IPC IPC(8): G06T7/00G06N3/04G06N3/08
CPCG06T7/0004G06N3/08G06T2207/30124G06N3/045
Inventor 陈海波段艺霖
Owner 深兰智能科技(上海)有限公司
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