LCD defect detection method based on feature pyramid convolutional neural network

A convolutional neural network and feature pyramid technology, applied in the field of target detection and recognition, computer vision, can solve problems such as ineffective use, inability to achieve efficient and accurate defect detection effects, and reduce the missed detection rate and false detection rate, The effect of improving detection efficiency

Inactive Publication Date: 2019-06-07
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Therefore, the existing detection methods of this kind cannot achieve efficient and accurate defect detection results, so that the LCD panel defect detection method based on deep learning is still unable to be effectively used in the actual production line, and it has become one of the problems that the entire industry needs to solve.

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  • LCD defect detection method based on feature pyramid convolutional neural network
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  • LCD defect detection method based on feature pyramid convolutional neural network

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[0026] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail in conjunction with specific embodiments and with reference to the accompanying drawings. The following examples are used to illustrate the present invention, but not to limit the scope of the present invention.

[0027] In the fields of machine learning and pattern recognition, it is generally necessary to divide samples into three independent parts-training sets, validation sets and test sets. Among them, the training set is used to train the model.

[0028] In the LCD defect detection method based on the feature pyramid convolutional neural network provided by the present invention, it is first necessary to collect LCD pictures of various defect types, and perform data processing and storage on the pictures, such as label (Label) defect types And record the frame data of defects (such as frame center positi...

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Abstract

The invention relates to an LCD defect detection method based on a feature pyramid convolutional neural network. The method comprises the steps of constructing a feature pyramid convolutional neural network and constructing a detection model, detecting an LCD panel picture by using the model; defect types and positions are screened and determined through output parameters. According to the method,respective advantages of the deep residual convolutional neural network and the feature pyramid network are fully utilized. According to the invention, the low-layer high-resolution image features and the high-layer high-semantic information features are effectively fused, and the type and position of the defect are directly obtained from the LCD picture by adopting single-stage detection, so that the detection efficiency is greatly improved, and the omission ratio and the false detection rate are reduced.

Description

Technical field [0001] The invention belongs to the technical field of computer vision, target detection and recognition, and particularly relates to an LCD defect detection method based on a feature pyramid convolutional neural network. Background technique [0002] At present, the LCD panels of mobile phones, computers and other displays mainly use Thin Film Transistor Liquid Crystal Display (TFT-LCD). It has the advantages of small size, thin thickness, low energy consumption, light weight, environmental protection and no radiation, and has quickly become the main material of display panels. [0003] Due to the current level of production technology, the manufacturer cannot guarantee that the LCD produced is perfect. As long as there are defects in any part of the production process, it will bring image display defects to the LCD, such as bright spots, dark spots, bright spot areas, splash screens, geometric distortion, color aberration (Mura), etc. Therefore, the LCD defect d...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62G06N3/04G02F1/13G09G3/00
Inventor 许国良范兴容刘恒彭大芹雒江涛
Owner CHONGQING UNIV OF POSTS & TELECOMM
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