A display defect detection method, includes: collecting at least one display image of at least one display to be detected; extracting a plurality of band-limited intrinsic mode function components from a display image in the at least one display image by using a complex variational mode decomposition method; extracting and fusing the plurality of band-limited intrinsic mode function components of the display image by using a convolutional neural network, so as to obtain an average brightness value and a brightness uniformity of the display image; and determining whether a display to be detected in the at least one display to be detected corresponding to the display image is qualified according to a preset classification rule and the average brightness value and the brightness uniformity of the display image.

A display panel includes a substrate including a display area in which elements used for display are disposed and a peripheral area, and an inspection element group disposed in the peripheral area of the substrate, where the inspection element group includes defects that are repeatedly disposed.

A system and method for defect detection. In some embodiments, the method includes: identifying, by a first neural network, a suspicious area in a first image; selecting, from among a set of defect-free reference images, by a second neural network, a defect-free reference image corresponding to the first image; identifying, by a third neural network, in the defect-free reference image, a reference region corresponding to the suspicious area; and determining, by a fourth neural network, a measure of similarity between the suspicious area and the reference region.

The disclosure provides a method for defect classification, including: extracting a low-level feature of a defect region from a defect image; encoding the low-level feature by using a defect dictionary to obtain a middle-level semantic feature corresponding to the low-level feature; classifying a defect in the defect region of the defect image into one of a plurality of defect categories based on the middle-level semantic feature, wherein the defect dictionary includes a defect intra-category dictionary and a defect inter-category dictionary.

According to one embodiment, an image processing device includes a hardware processor implemented by one or more processors. The hardware processor acquires a first image of a first object and a second image of a second object which is different from the first object, the images captured by an image capture processing device including an image capturing element and an optical system which images an object image on an image capturing surface of the image capturing element. The hardware processor measures a point spread function of the optical system based on the first image and the second image.

A method for detecting a screen is provided, which may improve detection accuracy of defective sub-pixels in the display screen. The method includes: obtaining an image of a screen to be detected; performing Gabor filtering on the image of the screen to be detected to obtain a plurality of Gabor filtered images; performing image fusion on the plurality of Gabor filtered images to obtain a fused image; segmenting the fused image by using different gray thresholds to obtain segmented images; and performing defect detection according to the segmented images to determine whether there is a defective sub-pixel in the screen to be detected. A value range of different gray thresholds is within a gray value range of the fused image.

A spot detecting apparatus includes a photographing part and a spot detecting part. The photographing part photographs, in a first resolution, an image displayed on a display panel to output first resolution image data, and photograph, in a second resolution, the image displayed on the display panel to output second resolution image data, where the second resolution is higher than the first resolution, and the image displayed on the display panel includes a first spot greater than or equal to a reference size and a second spot less than the reference size. The spot detecting part receives the first resolution image data and the second resolution image data, and subtracts the first resolution image data from the second resolution image data to detect the second spot.

An inspecting device of a display panel includes a contact including first probe pins that contact to data pads of a display panel and second probe pins that contact to common voltage pads of the display panel, a signal generator coupled to the first probe pins, the signal generator configured to generate a first data voltage corresponding to a first gray level and a second data voltage corresponding to a second gray level, a power generator coupled to the second probe pins, the power generator configured to generate a first common voltage and a second common voltage of which a voltage level is different from a voltage level of the second common voltage, and a defect detector configured to detect a defect of the display panel by removing a contact noise generated due to contact failure of the first probe pins and the second probe pins.

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.

An imaging apparatus an image sensor which generates a reference image data of a reference area that includes a measuring area in which a display panel is positioned and an image processor which determines the measuring area by analyzing luminance of the reference image data and generates a crop image data that includes a measuring image data of the measuring area.