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Defect classification method for display panel based on deep learning

A technology of display panel defects and deep learning, applied in neural learning methods, image analysis, image data processing and other directions, can solve problems such as low real-time performance, and achieve the effects of high accuracy, fast speed and good robustness

Active Publication Date: 2021-11-02
ZHAOQING ZHONGDAO OPTOELECTRONICS EQUIP CORP
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  • Application Information

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Problems solved by technology

The features of this method for extracting defect pictures are based on artificial experience, some features are actively selected, and each defect corresponds to a classification model. To judge a target image, it needs to go through the judgment of each defect model, real-time Low

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  • Defect classification method for display panel based on deep learning
  • Defect classification method for display panel based on deep learning
  • Defect classification method for display panel based on deep learning

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Embodiment Construction

[0040] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0041] The invention discloses a classification method for display panel defects based on deep learning, which includes the following steps: for the defects detected by AOI automatic optical inspection equipment on the display panel, some false defects are often detected due to the accuracy of the equipment. Defects, therefore, first divide the collected images into two groups (defective and non-defective) according to whether there...

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Abstract

The invention discloses a defect classification method for a display panel based on deep learning. The method comprises the following steps: obtaining at least one suspected defect picture through automatic optical detection; inputting the suspected defect picture into a first classification model, wherein the first classification model is used for judging whether the suspected defect picture is a real defect or a false defect; and if the suspected defect picture is a real defect, inputting the suspected defect picture into a second classification model which is used for judging whether the suspected defect picture is a black defect or a white defect. According to the method, the classification model is trained based on the convolutional neural network of deep learning, the defect pictures detected by the AOI on the display panel are automatically classified in a classifier cascade mode, the precision is high, the speed is high, and the robustness is good.

Description

technical field [0001] The invention belongs to the technical field of machine vision automatic classification, and relates to a classification method for display panel defects based on deep learning, more specifically, to a classification model obtained by using defect pictures and deep learning technology training to classify target defect images method for classification. Background technique [0002] The Chinese full name of AOI (Automated Optical Inspection) is automatic optical inspection, which is a device based on optical principles to detect common defects encountered in welding production. AOI is a new type of testing technology emerging, but it is developing rapidly, and many manufacturers have launched AOI testing equipment. During automatic detection, the machine automatically scans the PCB through the camera, collects images, compares the tested solder joints with the qualified parameters in the database, and after image processing, checks out the defects on t...

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Application Information

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IPC IPC(8): G06T7/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06T7/0004G06N3/08G06T2207/10004G06T2207/20076G06T2207/20081G06T2207/20084G06T2207/30141G06N3/047G06N3/048G06N3/045G06F18/241G06F18/2415
Inventor 左右祥杨义禄李波关玉萍查世华
Owner ZHAOQING ZHONGDAO OPTOELECTRONICS EQUIP CORP
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