Abstract: The present disclosure provides a method for controlling a rotating speed, a cleaning robot, and a storage medium. The method includes: obtaining the rotating speed of the main brush when the main brush cleans a surface-to-be-cleaned; comparing the rotating speed of the main brush with a predetermined rotating speed; decreasing the rotating speed when the rotating speed is greater than the predetermined rotating speed; or, increasing the rotating speed when the rotating speed is less than the predetermined rotating speed. As such, over-load of an electric motor of the main brush can be avoided, thereby extending the service life of the electric motor. In the meantime, the power consumption by the cleaning robot can be reduced, thereby extending the operation time of the cleaning robot. Furthermore, a satisfying cleaning effect of the cleaning robot can be maintained.

Abstract: A robotic cleaning system may include a docking station, a robotic cleaner that includes at least one of a first set of robot charging contacts or a second set of robot charging contacts, the first and second sets of robot charging contacts being configured to electrically couple the robotic cleaner to the docking station, a dust cup configured to removably couple to the robotic cleaner, and a mop module configured to removably couple to the robotic cleaner. When the mop module is coupled to the robotic cleaner, the robotic cleaner may be configured to electrically couple to the docking station using the first set of robot charging contacts. When the mop module is not coupled to the robotic cleaner, the robotic cleaner may be configured to electrically couple to the docking station using the second set of robot charging contacts.

Abstract: A chromaticity measurement method and device for calibration of a tiled LED display screen (A) having the advantages of convenience in operation and accurate measurement. The chromaticity measurement method includes: imaging emergent light from the tiled LED display screen (A) with an objective lens (1); receiving a light beam from the objective lens (1) with a transflective lens (2) and performing beam splitting to obtain a first light beam and a second light beam; receiving the first light beam with an imaging sensor (3) to obtain an original image; receiving the second light beam in a two-dimensional lattice form with a movable optical fiber array coupling device (4), and converting the second light beam into one-dimensional lattice light; and obtaining chromaticity information and spectrum information of a plurality of regions on the tiled LED display screen (A) according to the one-dimensional lattice light with a spectrum acquisition device (5).

Abstract: An automatic optical inspection device, including an image storage unit; an image computing unit; and an image acquisition unit. The image storage unit includes a first communication interface and a second communication interface. The image computing unit includes a first optical interface, a second optical interface, a third optical interface, and a fourth optical interface; the image acquisition unit includes a third communication interface and a camera interface. The image storage unit is configured to transmit configuration parameters and test commands to the image computing unit, receive a test result transmitted from the image computing unit via the first communication interface, and receive data from the image acquisition unit via the second communication interface. The image computing unit is configured to receive the configuration parameters and test commands from the image storage unit, and transmit the test result to the image storage unit via the first fiber interface.

Abstract: The invention discloses a method for evaluating a brightness measurement accuracy of a Demura equipment by generating a noise-added image by adding a random number to an original grayscale image and obtaining a measurement value corresponding to the added random number via the noise-added image displayed by the Demura equipment. The brightness measurement accuracy of the Demura equipment is evaluated based on a correlation between the measured value and the originally added random number to measure a brightness extraction performance of the Demura equipment. The method provided by the invention provides a quantitative and measurable determining method that may better reflect the actual performance of the Demura equipment.

Abstract: The invention discloses a method for evaluating a brightness measurement accuracy of a Demura equipment by generating a noise-added image by adding a random number to an original grayscale image and obtaining a measurement value corresponding to the added random number via the noise-added image displayed by the Demura equipment. The brightness measurement accuracy of the Demura equipment is evaluated based on a correlation between the measured value and the originally added random number to measure a brightness extraction performance of the Demura equipment. The method provided by the invention provides a quantitative and measurable determining method that may better reflect the actual performance of the Demura equipment.

Abstract: An automatic optical inspection device, including an image storage unit; an image computing unit; and an image acquisition unit. The image storage unit includes a first communication interface and a second communication interface. The image computing unit includes a first optical interface, a second optical interface, a third optical interface, and a fourth optical interface; the image acquisition unit includes a third communication interface and a camera interface. The image storage unit is configured to transmit configuration parameters and test commands to the image computing unit, receive a test result transmitted from the image computing unit via the first communication interface, and receive data from the image acquisition unit via the second communication interface. The image computing unit is configured to receive the configuration parameters and test commands from the image storage unit, and transmit the test result to the image storage unit via the first fiber interface.

Abstract: The disclosure provides a background suppression method and a detecting device in automatic optical detection for a display panel, wherein the background suppression method includes the following steps: S1: collecting a pure color image of the display panel; S2: performing multi-level wavelet decomposition on the pure color image to obtain a series of high-frequency sub-bands and low-frequency sub-bands; S3: performing coefficient smoothing process on high-frequency sub-bands in multiple directions of each level, and performing contrast enhancement process on each level of low-frequency sub-bands; S4: the processed high-frequency sub-bands and the processed low-frequency sub-bands are subjected to wavelet reconstruction to obtain a defect image after background suppression.

Abstract: The disclosure provides a background suppression method and a detecting device in automatic optical detection for a display panel, wherein the background suppression method includes the following steps: S1: collecting a pure color image of the display panel; S2: performing multi-level wavelet decomposition on the pure color image to obtain a series of high-frequency sub-bands and low-frequency sub-bands; S3: performing coefficient smoothing process on high-frequency sub-bands in multiple directions of each level, and performing contrast enhancement process on each level of low-frequency sub-bands; S4: the processed high-frequency sub-bands and the processed low-frequency sub-bands are subjected to wavelet reconstruction to obtain a defect image after background suppression.

Abstract: The present disclosure discloses a GPU-based TFT-LCD Mura defect detection method, comprising: (1) establishing a studentized residual based double-second-order regression diagnosis model based original image data to obtain double-second-order regression background data; (2) obtaining influence quantities of respective data points on fitted values according to the original image data and the double-second-order regression background image data; (3) excluding outliers and influential points in the original image data according to the influence quantities to obtain a new pixel point set; (4) establishing a double-N-order polynomial surface fitting model according to the new pixel point set to obtain double-N-order background image data; (5) obtaining a residual image R according to the double-N-order background image data and the original image data, and performing threshold segmentation on the residual image to obtain a threshold segmentation image; and (6) performing morphological processing on the threshold

Abstract: The present disclosure discloses a GPU-based TFT-LCD Mura defect detection method, comprising: (1) establishing a studentized residual based double-second-order regression diagnosis model based original image data to obtain double-second-order regression background data; (2) obtaining influence quantities of respective data points on fitted values according to the original image data and the double-second-order regression background image data; (3) excluding outliers and influential points in the original image data according to the influence quantities to obtain a new pixel point set; (4) establishing a double-N-order polynomial surface fitting model according to the new pixel point set to obtain double-N-order background image data; (5) obtaining a residual image R according to the double-N-order background image data and the original image data, and performing threshold segmentation on the residual image to obtain a threshold segmentation image; and (6) performing morphological processing on the threshold