The present invention provides an alerting method for monitoring a color shift of a display panel. The alerting method includes: generating a grayscale waveform diagram for an entire frame according to a normal pixel pattern of the display panel, the grayscale waveform diagram having waveform setting values; generating a grayscale waveform diagram for an entire frame according to a pixel pattern of a display panel to be tested, the grayscale waveform having waveform reference values; and obtaining a predetermined difference value between each of the waveform reference values and each of the waveform setting values, and sending out an alerting message when the predetermined difference value exceeds a specified value.

This application discloses an inspection method and an inspection machine for a display panel. The inspection method includes the steps of: taking a picture of a to-be-inspected display panel to obtain a to-be-inspected image of the to-be-inspected display panel; removing high-frequency information in the to-be-inspected image to obtain a high-frequency-removed image; calculating a difference between the to-be-inspected image and the high-frequency-removed image to obtain a difference image; making a determination regarding the difference image by comparing the difference image with a preset threshold, determining that the display panel fails the inspection if the difference image exceeds the preset threshold, and determining that the display panel passes the inspection if the difference image does not exceed the preset threshold.

A spacer supportability evaluation method and device as well as a computer readable storage medium are provided. The method includes acquiring initial distribution images of spacers and corresponding support pads on a substrate, performing binary grayscaling processing to obtain distribution images of spacers and corresponding support pads, obtaining two binary matrices according to the distribution images, subjecting the two binary matrices to convolution in a spatial domain or to multiplication in a frequency domain to obtain an equivalent support matrix, calculating a number of elements in the equivalent support matrix whose values are a first value to obtain a number of supported pixels. The supportability of spacers is evaluated by acquiring parameters or design drawings of the spacers to calculate suitable size and positional arrangement of each spacer, improving the supportability of spacers and keeps the cell gap of the liquid crystal cell stable and uniform.

A defect detection method for an LCD screen includes acquiring a screen image of the LCD screen, performing a rough search for defects in the screen image to extract a suspected area where the defects are located, (based on the suspected area,) clustering every pixel point in the suspected area to obtain clustering results, and each clustering result corresponds to a suspected defect, and (according to the clustering result,) calculating a width and length of the suspected defect corresponding to the clustering result, and determining whether the suspected defect is a screen defect and which type of screen defect it belongs to based on the width and length of the suspected defect. The technical solution of the present disclosure realizes automatic detection of screen defects by rough positioning and accurate positioning of suspected defects, and the detection results are accurate and reliable.

A method of building a model of defect inspection for a light-emitting diode (LED) display is adapted to be implemented by a model-building system. The model-building system stores captured images respectively of LED displays that were displaying images. Each of the captured images corresponds to a status tag that indicates a status of the image being displayed by the respective one of the LED displays. The method includes: performing data preprocessing on the captured images to result in pieces of pre-processed data that respectively correspond to the captured images; and building a model of defect inspection by using an algorithm of machine learning based on the pieces of pre-processed data and the status tags.

A device for inspecting a display device includes a camera to photograph a substrate and generate image information, a pixel value setter to set pixel values corresponding to respective luminances of a plurality of pixels from the image information, and to detect a crack region based on the pixel values, a stress calculator to calculate a critical stress of a crack included in the crack region, and a determiner to check whether the critical stress is equal to or greater than a first threshold value and to determine whether the substrate has defects. The stress calculator calculates a critical stress of the substrate by using fracture toughness, a shape factor, and a crack depth. The shape factor is set to increase as a compressive stress of the substrate increases.

A system for manufacturing defect classification is presented. The system includes a first neural network receiving a first data as input and generating a first output, a second neural network receiving a second data as input and generating a second output, wherein first neural network and the second neural network are trained independently from each other, and a fusion neural network receiving the first output and the second output and generating a classification. The first data and the second data do not have to be aligned. Hence, the system and method of this disclosure allows various type of data that are collected during manufacturing to be used in defect classification.

Disclosed are a polarizer attachment detection method and device, and a display device. The polarizer attachment detection method includes: controlling an image collection device to collect image data after attachment of a polarizer in a current detection mode; in response to the image data in the current detection mode failing to match prestored standard image data, switching to a next detection mode, controlling the image collection device to collect image data after the attachment of the polarizer in the next detection mode, and marking the image data collected in the next detection mode as new image data; in response to the new image data matching the prestored standard image data, outputting result information that the attachment is correct; and in response to the new image data failing to match the prestored standard image data, outputting result information that the attachment is incorrect.

An object of the present invention is to provide an information processing method and a computer program that can suppress an increase in inspection time in the manufacturing process of the monitors. The present invention provides an information processing method comprising: an error calculation step of calculating an error between input image data input to an autoencoder and output image data output from the autoencoder; a similarity calculation step of calculating a similarity between compressed data and reference data based on the compressed data and the reference data, the compressed data being acquired by compressing the input image data in an encoder of the autoencoder; and a determination step of determining whether a display unevenness of the input image data is acceptable based on a relationship between the error and the similarity, the relationship corresponding to a relational expression or a table.

Embodiments of the present invention facilitate product defect detection. A computer-implemented method comprises: receiving, by a device operatively coupled to one or more processors, a template image of a normal product; generating, by the device, one or more geometric training parameters for transforming the template image; and transforming, by the device, the template image using the one or more geometric training parameters to generate a transformed image for training a data model, wherein the trained data model being used for aligning the template image and an image under inspection of a product.