3005 results about "Stain" patented technology

The invention discloses an unmanned car washer stain panorama identification system. The system operation process specifically comprises the following steps: acquiring panorama image data of a target car; preprocessing the panoramic image data to obtain a standardized panoramic image set; performing stain area identification on the standardized panoramic image set based on a deep learning model to generate an initial stain distribution diagram; performing stain type classification on the initial stain distribution diagram according to a stain feature database to generate a stain classification result set; generating a dynamic cleaning path instruction set based on the stain classification result set and a cleaning strategy library; real-time images in the cleaning process are collected in real time, real-time stain residue analysis is conducted, and finally a cleaning effect feedback report is generated. The method has the following advantages and effects that the system of multi-dimensional stain feature recognition, classification and dynamic decision can be fused, so that the core contradiction that the cleaning strategy is not matched with the stain features in the prior art is solved.

The invention relates to a high-altitude camera intelligent cleaning method based on an unmanned aerial vehicle, and the method comprises the following steps: S1, setting an unmanned aerial vehicle intelligent cleaning system, S2, obtaining the omnibearing image of a to-be-cleaned high-altitude camera through a control system and a high-definition camera of the unmanned aerial vehicle, S3, introducing a multi-scale attention mechanism to improve a YOLO algorithm, carrying out the detection of smudge points of the image, and carrying out the cleaning of the smudge points of the to-be-cleaned high-altitude camera. S4, the recognized stain points are converted into three-dimensional space coordinates, the dip angle of a spray head is adjusted, and a central processing system starts a spraying system to conduct precise positioning cleaning, S5, after cleaning, the unmanned aerial vehicle is controlled to conduct all-dimensional photographing on the cleaned camera, key point detection of the stain points is conducted on the obtained picture again, and S6, if no stain point is detected, the unmanned aerial vehicle is controlled to conduct all-dimensional photographing on the camera. Controlling the unmanned aerial vehicle to return to complete cleaning; and if the smudge points are detected, all-around secondary cleaning is carried out on the smudge points until the smudge points do not exist, and cleaning is completed. The cleaning quality can be improved, the cost is saved, and the risk is reduced.

The invention discloses a visual target tracking method and system for an unmanned aerial vehicle, and relates to the technical field of image acquisition and processing.The method comprises the steps that firstly, a bridge area is scanned in a directional mode in multiple different sun incident angle periods (the interval is larger than or equal to 30 degrees), and stain interference is eliminated through natural light multi-angle changes; the geometric stability of the crack edge is quantified by constructing a gradient direction distribution matrix of the time dimension; secondly, dividing multiple groups of scanning data into at least six direction groups, calculating pixel proportion variances group by group, accumulating the pixel proportion variances into a total variance parameter, and effectively distinguishing cracks from surface stains by combining a direction consistency verification mechanism (the total variance lt is judged as cracks by a preset threshold value); and finally, establishing a dynamic feedback closed loop based on a historical variance parameter sample library, adaptively adjusting a threshold value through a moving average value deviation ratio formula, and solving the problem of false detection caused by material aging and environmental fluctuation.

The invention discloses an automobile cushion sponge quality detection method based on artificial intelligence, and particularly relates to the field of quality detection.The method comprises the steps that sponge full-surface data is obtained and preprocessed, then detection units are divided according to double-layer grids, surface damage, stains and density uniformity are detected based on an artificial intelligence model, and preliminary screening is executed; if not, stopping the detection; after the primary screening is qualified, collecting internal structure data of the sponge, extracting defect characteristics, and carrying out a cyclic indentation test to obtain physical performance parameters; single-mode quality functions of the surface, the internal structure and the physical performance are constructed respectively, after function data are preprocessed and subjected to environment correction, primary filtering is performed firstly, then a D-S evidence theory is applied to perform multi-mode fusion decision so as to output a final quality judgment result, and meanwhile, detection data feedback optimization is combined to form a quality control closed loop, so that the quality control accuracy is improved. And intelligent and accurate detection of the quality of the automobile cushion sponge is realized.

The invention relates to the technical field of swimming pool cleaning equipment control, and provides a cleaning mode switching method and device for a multifunctional swimming pool cleaning robot. According to the method, water quality data corresponding to a swimming pool, stain distribution images of pool walls and pool bottoms, swimming pool structure size data and stain type data are collected in real time; dividing a cleaning area according to the swimming pool structure size data, generating a stain area marking result in the cleaning area in combination with the stain distribution image and the stain type data, and judging the pollution level of the whole swimming pool according to the water quality data so as to retrieve a corresponding cleaning mode from a preset cleaning mode library, the preset cleaning mode library comprises a global coverage mode, a key enhancement mode, a corner fitting mode and a water quality maintenance mode; and according to the called cleaning mode, a corresponding cleaning path plan and execution instruction is generated, and the driving module, the scrubbing module and the purification module are controlled to work cooperatively. According to the method, accurate analysis of the swimming pool environment is achieved, and blind cleaning of a traditional fixed mode is avoided.

The invention relates to the technical field of fin cleaning, in particular to a robot energy-saving system and method for intelligent control over the cold end of an indirect cooling auxiliary unit, and the system comprises the steps that a fin array surface temperature abnormal area and a stain coverage area are extracted, and a self-adaptive grid is divided according to the stain density; defining a local cleaning strategy and a global cleaning strategy based on the adaptive grid; generating a spiral traversal path based on an adaptive grid and a global cleaning strategy, and optimizing the shortest advancing path of the robot; and the robot moves in the working space according to the shortest advancing path, the local cleaning strategy is executed in sequence, and intelligent energy-saving cleaning of the fins of the indirect cooling auxiliary unit is achieved.

A deep learning-based digital / virtual staining method and system enables the creation of digitally / virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally / virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital / virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally / virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.

The invention discloses an intelligent cleaning method and system for an engineering vehicle, and the method comprises the steps: capturing a vehicle surface image of a current to-be-cleaned engineering vehicle, carrying out the preprocessing of the vehicle surface image, and obtaining vehicle image data; according to the vehicle image data and the trained machine learning model, obtaining a stain identification result and vehicle type information of the engineering vehicle; based on a stain identification result and vehicle type information, an optimal cleaning scheme is comprehensively calculated, and cleaning parameters are intelligently adjusted; the cleaning state is monitored in real time, abnormal conditions are detected, and when water source insufficiency and machine faults occur, an alarm is given out immediately, and corresponding treatment measures are taken. The cleaning efficiency of the engineering vehicle is remarkably improved, the cleaning time is shortened, and the labor cost is reduced; through the synergistic effect of stain analysis and intelligent cleaning, targeted cleaning and comprehensive deep cleaning are achieved, and the cleaning quality is improved.

The invention relates to the technical field of inorganic non-metal coating materials, in particular to high-stain-resistance enamel matte glaze and a preparation method thereof.The high-stain-resistance enamel matte glaze is prepared from selected quartz sand, feldspar and other basic raw materials by adding a composite additive Y and a rare earth activator Z. The preparation method comprises the steps that the basic raw materials are mixed and subjected to ball milling, then the additives are added for continuous ball milling, high-temperature melting, water quenching and secondary ball milling are conducted, and the high-stain-resistance enamel matte glaze is obtained. The granularity is controlled, and the stain resistance is improved. The enamel matte glaze has the advantages of obvious advantages, high stain resistance, difficulty in stain attachment, easiness in cleaning, capability of prolonging the service life of a product and reducing the maintenance cost, photocatalytic self-cleaning and environment-friendly characteristics, good matte effect and capability of improving the competitiveness of the product.

The invention relates to an unmanned aerial vehicle lens stain identification and cleaning method and system based on deep learning. The method comprises the following steps: generating an image acquisition instruction according to a set sampling period to obtain a to-be-detected image; the image acquisition instruction is used for instructing an unmanned aerial vehicle camera to perform image acquisition operation and feeding back in the form of a to-be-detected image; preprocessing the to-be-detected image to obtain a to-be-detected standard image; inputting a to-be-detected standard image into the stain detection model to obtain a stain identification result; the stain identification result comprises a central coordinate of the polluted area, bounding box data, a confidence coefficient score and a stain type label; generating a cleaning parameter and a cleaning instruction according to the stain identification result; the cleaning instruction is used for instructing the cleaning equipment to clean the unmanned aerial vehicle lens according to the cleaning parameters. By adopting the method, the problems of difficult stain identification and extensive cleaning decision in an existing unmanned aerial vehicle visual system can be solved, and the accuracy of stain detection and the intelligence of cleaning control are realized.

The invention provides a photovoltaic power station cleaning robot path planning method, medium and system, and belongs to the technical field of photovoltaic power station operation and maintaining.The photovoltaic power station cleaning robot path planning method includes the steps that firstly, a photovoltaic panel grid division model is established, and stain distribution information is obtained through the image processing technology to construct a stain distribution matrix; secondly, calculating a cleaning water demand matrix based on stain distribution, and meanwhile, considering an influence relationship among vertical column grid units; determining the cleaning duration by combining the efficiency of the cleaning device; constructing a robot energy consumption model; calculating an optimal cleaning path by applying a resource optimization distribution function; integrating the environmental parameters and inputting the environmental parameters into a photovoltaic cleaning neural network model to predict the actual cleaning difficulty; a cleaning strategy is dynamically adjusted accordingly; and executing the final path and recording data to update the related model. According to the method, through real-time sensing and analysis of the stain distribution characteristics, intelligence of path planning of the cleaning robot is realized, and the technical problem that the cleaning robot cannot perform intelligent path planning according to the surface stain distribution characteristics of the photovoltaic panel is solved.

The invention provides an environment-considered photovoltaic panel cleaning scheme determination method, and belongs to the technical field of photovoltaic panel cleaning.The method comprises the steps that firstly, real-time weather data and prediction data are obtained, and an environment stain correlation model is established; a multispectral camera is adopted to identify the types of stains on the surface of the photovoltaic panel and calculate a distribution map; calculating a cleaning index by combining the generating capacity data and the solar irradiation intensity, and determining a cleaning priority; a weather adaptability scoring system is constructed, and a robot operation environment threshold value is set; dividing power generation time periods according to the sunlight intensity and calculating an optimal cleaning time window; cleaning schemes are matched according to different stain types, and parameters are adjusted according to environmental conditions; calling a stain cleaning efficiency optimization function to carry out multi-objective optimization; and finally, generating an optimal cleaning path and parameter configuration by utilizing the photovoltaic cleaning planning network model. The technical problem that a photovoltaic panel cleaning scheme cannot be adaptively optimized according to complex and changeable environmental factors is solved.

The invention discloses an intelligent self-adaptive cleaning system and method for a photovoltaic module, and mainly solves the problems that the cleaning effect of a photovoltaic module cleaning system on stains (such as bird droppings and oil stains) with strong adhesive force and fine dust particles is limited, and it is difficult to ensure that the surface of the module keeps good cleanliness all the time. The cleaning system comprises an environment sensing module used for detecting the cleaning state of the photovoltaic module, an intelligent cleaning module connected with the environment sensing module and used for executing the cleaning function, and self-adaptive control connected with the environment sensing module and used for controlling the intelligent cleaning module. And the energy recovery module is connected with the intelligent cleaning module and is used for realizing energy recovery and feeding back power supply to the environment sensing module. By means of the design, the multi-sensor fusion environment sensing technology and the intelligent cleaning technology are combined, the stain type and the pollution degree can be more accurately recognized, a targeted cleaning mode is adopted, and compared with a traditional cleaning technology, the cleaning effect is remarkably improved.

The utility model discloses a cleaning needle for removing stains on the inner wall of a flared pipe. The cleaning needle is characterized by comprising a needle seat, a needle rod integrally connected with the needle seat, and a needle cone positioned at the end part of the needle rod, the appearance of the needle base is larger than the outer diameter of the needle rod, a compressed air injection hole is formed in the needle base and the needle rod in the axial direction, an air injection hole is formed in the bottom end of the compressed air injection hole, and the needle rod is longer than a flaring forming inner hole of a machining pipe. A series of problems of complicated production process, sewage and VOC discharge, low production efficiency, high cost and the like of traditional technologies such as water-based cleaning agent cleaning and solvent cleaning are solved, stains are cleaned, the structure is simple, one cleaning needle can be used for a long time, hardware such as a compressor can be integrated into other compressed air nets of a workshop after being put into one time, and the cost is low. No special operation environment requirement exists, and the comprehensive cost is low.

The embodiment of the invention discloses a taint analysis method and device, computer equipment and a storage medium, and belongs to the technical field of information security. In the application, the coprocessor of the computer equipment comprises a plurality of registers, and the plurality of registers not only can be used for storing stain information corresponding to a plurality of general registers in a main processor of the computer equipment, but also can be used for storing stain information corresponding to a program memory space of a target program currently executed by the main processor. Therefore, when the coprocessor performs taint propagation on the first instruction, the taint information of the operand of the first instruction can be accessed by accessing the plurality of registers, and compared with the mode of accessing the taint information by accessing a memory, the operation is simpler, the speed is faster, and the performance overhead is smaller.

The invention discloses equipment and a method for detecting the strength of an annular section of a large-diameter TA31 pipe, and relates to the technical field of pipes. According to the annular section strength detection equipment for the large-diameter TA31 pipe, through the design of a composite mechanism, the pipe is placed in the processing mechanism, the pipe space is restrained through the limiting mechanism, the pipe is prevented from moving in the inspection process, danger to workers is avoided, and therefore normal operation of the equipment is kept; the fixing mechanism controls the pipe to rotate and rub with the processing mechanism, so that the effect of cleaning the surface of the pipe is achieved, the situation that stains on the surface of the pipe affect the detection effect is avoided, a hydraulic rod controls a pressing plate to extrude the surface of the pipe, and the detection effect on the annular section strength of the pipe is achieved; therefore, the friction force on the surface of the pipe is increased, the pipe is prevented from shaking in the downward pressing process, and the operation stability is improved.

The invention belongs to the technical field of crop molecular markers, and particularly discloses a molecular marker related to the waterlogging tolerance of brassica napus and application of the molecular marker. According to the invention, a 1.2 Kb transposon insertion and a 2.6 Kb transposon insertion are identified by utilizing GWAS (Good White Attenual Sequence) and are positioned in a promoter region of the Bna.C08. GUN4 gene, and the rape containing the two transposon insertion has high waterlogging resistance. The molecular marker is used for auxiliary selection of oilseed rape with strong waterlogging resistance, whether the oilseed rape germplasm is waterlogging-resistant oilseed rape germplasm can be predicted only by detecting the characteristic band of the molecular marker, the identification method is simple and feasible, the selection efficiency is high, and the molecular marker has huge application potential in the field of oilseed rape waterlogging resistance improvement breeding.

The invention discloses a high-speed strip steel defect detection method and system based on anomaly enhancement and image reconstruction, the problem that part of defects are difficult to recognize is solved by adopting a pseudo anomaly enhancement method, the defects difficult to detect are divided into a scratch type and a stain type, a local average gray level floating method is designed to generate scratch type pseudo anomalies, and the defect detection accuracy is improved. And a contour random disturbance method is used to generate stain type pseudo anomalies, and local difference features are provided for the model. And a lightweight image reconstruction model of an encoder-decoder structure is designed to achieve high detection speed and meet the requirement of synchronous detection. According to the method, performance testing is carried out on a strip steel surface image data set, advanced performance evaluation is obtained, and the effectiveness of the method is proved.

The invention relates to the technical field of visual identification, and provides an AI visual system for shaft part stain identification and cleaning equipment, an image acquisition module acquires a surface image of a shaft part, generates an image of the shaft part, captures the surface image of the shaft part in real time, and provides a data basis for subsequent stain identification; the stain identification module processes the shaft part image, identifies the local contrast of each shaft sub-block in the shaft part image to obtain a stain candidate area, detects whether a stain edge exists in each shaft sub-block in the stain candidate area, combines the shaft sub-blocks to obtain a stain area, and triggers an equipment signal; the real-time monitoring and identification capability enables the production process to be more efficient, and problems can be found and processed in time. Corresponding cleaning operation can be automatically executed according to equipment signals of the visual system, the shaft parts are cleaned, manual intervention is reduced through the automatic process, and the production efficiency and the product quality are improved.

The invention discloses a stain identification method and equipment applied to a photovoltaic panel and a medium, belongs to the technical field of machine vision, and is used for solving the technical problem that stains on the photovoltaic panel cannot be accurately identified in the prior art. The method comprises the following steps: preprocessing an original image in a photovoltaic panel video to generate a preprocessed image; using a preset generative adversarial network model to generate a stain-free reference image according to the preprocessed image; extracting a part with the gray level higher than a preset gray level threshold value in the blot-free reference image as a mask, and reducing the gray level of a part corresponding to the mask in the preprocessed image to be below the preset gray level threshold value to obtain a secondary processing image; in the secondary processing image, extracting a highlight area of which the gray level is higher than a preset gray level threshold value, and segmenting the highlight area into a plurality of areas to be marked by using a preset area segmentation algorithm; and calculating a velocity vector corresponding to each to-be-marked area based on a sparse optical flow algorithm, and marking. Through the above steps, the method can achieve the precise recognition of the stains on the photovoltaic panel.

The invention relates to a saccharifying pot cleaning method and system and an intelligent terminal, and relates to the technical field of intelligent cleaning, and the method comprises the following steps: obtaining a working signal of a saccharifying pot; based on the condition that the working signal is consistent with the standby signal, a cleaning device is controlled to enter the saccharifying pot to roughly clean the interior of the pot according to the rough cleaning force value; acquiring image information in the pot; frame-selecting and marking a stubborn stain area from a stain database according to the in-pot image information and the stain characteristics; a shoveling device is controlled to go to the marked position to shovel stubborn stains in the stubborn stain area according to the shoveling force value, and after shoveling, image information after shoveling is obtained; on the basis of the condition that the shoveled image information contains the stain residual characteristics, the decontamination equipment is controlled to remove the stain residual characteristics through a decontamination method; and after the stain residual characteristics are removed, the cleaning device is controlled to integrally wash the interior of the saccharifying pot. The method has the effect of improving the working efficiency.

A surface cleaning identification method for titanium alloy bar rolling relates to the technical field of image identification, and comprises the following steps: graying a global image of the surface of a titanium alloy bar to generate a grayscale image, and carrying out texture feature analysis on the grayscale image to obtain a global noise index of the grayscale image; based on the global noise index, obtaining a noise difference coefficient in a neighborhood range of each pixel point, and performing adaptive filtering on the grayscale image according to the noise difference coefficient to obtain a denoised grayscale image; performing gray change coefficient analysis on the denoised gray image, dividing the gray image into an abnormal sliding window or a normal sliding window, and performing local feature merging and erosion operation on each abnormal sliding window in the denoised gray image; and obtaining the stain type corresponding to the abnormal sliding window which completes local feature combination and erosion operation. According to the method, the efficiency and the accuracy of surface cleaning identification of titanium alloy bar rolling are remarkably improved.

The invention provides an offshore wind power blade-oriented stain following and unmanned aerial vehicle disturbance detection method, which comprises the following steps of: acquiring a fan blade image by using an unmanned aerial vehicle airborne camera, and preprocessing the fan blade image to obtain a fan blade stain track; performing time sequence processing on the stain track of the fan blade, extracting time and space characteristics of the stain track, decoupling an unmanned aerial vehicle offshore wind power blade cleaning interference source, and obtaining a disturbance detection result; and according to a disturbance detection result, an unmanned aerial vehicle flight control strategy is adjusted, and the stability of the unmanned aerial vehicle during fan blade stain cleaning is kept. According to the method, a plurality of disturbance identification modes are adopted for track time sequence processing from visual identification, identification is carried out without depending on a sensor of the unmanned aerial vehicle, the disturbance type is directly identified through the position change of the stain, the change in a time sequence signal has high identifiability and decoupling performance on the track, disturbance identification is efficiently carried out, and the disturbance identification efficiency is improved. And meanwhile, the unmanned aerial vehicle follows the blade positions.

The invention relates to the technical field of unmanned aerial vehicle intelligent maintenance and cleaning, in particular to an unmanned aerial vehicle-based main road camera non-closure autonomous cleaning system and method, and the system executes the following steps: an unmanned aerial vehicle hovers at the outer side of a camera, collects millimeter wave-polarization-acoustic signals to generate stain spectrum fingerprints, and obtains cleaning parameters through optimization; and the model prediction controller sequentially executes plasma, acoustic cavitation and ion air curtain dry cleaning under energy constraint, self-adjusting parameter iteration is carried out when residues exceed a threshold value, and return flight and battery replacement are carried out after the standard is reached. And the cleaning progress, the residual index and the energy consumption are packaged and chained, the cloud self-learning model updates online and outputs a next order of instruction, and non-stop, low-consumption and efficient autonomous cleaning is realized.

The invention relates to the technical field of image data processing, in particular to a stain detection method for fresh egg cleaning, and the method comprises the steps: determining the gradient direction entropy of a pixel point according to the entropy sum of the number of pixel points located in each gradient direction angle interval in a pixel point initial window in a fresh egg gray image; according to the horizontal component sum and the vertical component sum of the gradient direction angles of all the pixel points in the pixel point initial window, calculating the local geometric direction consistency of the pixel points; calculating the size of a target window of the pixel point; the segmentation threshold value of the pixel point is calculated according to the gray average value and the gray standard deviation of all the pixel points in the pixel point target window, the fresh egg gray image is processed according to the segmentation threshold value of the pixel point, a fresh egg gray image stain detection result is obtained, and the accuracy of the fresh egg stain detection result is effectively improved.

The invention belongs to the technical field of oil stain detection, and particularly relates to a water surface oil stain detection method, system and equipment, the water surface oil stain detection system comprises a flexible circuit board FPC, an InGaAs infrared sensor, an infrared narrow-band filter and an optical lens, light enters a visible light camera and the optical lens through an external lens, the visible light camera collects visible light images, and the visible light images are transmitted to the InGaAs infrared sensor; the optical lens focuses a light beam, then light with a specific wavelength is screened out through the near-infrared narrow-band filter, the screened light is received by the InGaAs infrared sensor, and the InGaAs infrared sensor is connected with the FPC; the method has the advantages that visible light imaging and short wave infrared spectrum analysis technologies are ingeniously fused, so that the accuracy and the reliability of oil stain detection are remarkably improved. Under the current background that environmental protection and water resource management are increasingly severe, powerful technical support is undoubtedly provided for water surface pollution monitoring.

The invention provides a cleaning method and device, electronic equipment, a storage medium and a vehicle, and relates to the technical field of vehicles. The method comprises the following steps: carrying out stain identification on a target image corresponding to the windshield glass; determining a first position of the stain in the windshield glass under the condition of identifying that the stain exists in the target image; the spraying point of the cleaning equipment is adjusted to the first position, the cleaning equipment is controlled to spray the cleaning liquid, the cleaning liquid can be accurately sprayed on the stains by adjusting the rotating angle of the spraying point, and the effect of rapidly cleaning the stubborn stains on the windshield glass is achieved.

The invention provides a steel plate surface defect detection method and system based on multi-source data fusion, and relates to the technical field of steel production automatic detection.The method comprises the steps that water stain pretreatment is conducted on the surface of a steel plate, the residual humidity is reduced to be below a preset low humidity threshold value, and a dry surface is obtained; based on the dry surface, collecting multi-source data through a 3D laser line scanning camera and a 2D industrial line scanning camera which are synchronously triggered, obtaining line scanning laser point cloud data, a line scanning reflectivity gray level image, a line scanning depth gray level image and an area array reflectivity color image, and establishing a space corresponding relation among the multi-source data; and forming a multi-source data set based on the spatial correspondence among the multi-source data. According to the method, a high-precision and high-robustness steel plate surface defect detection system covering the whole detection process is constructed by eliminating water stain interference, unifying a multi-source data reference, optimizing data quality, accurately identifying defects and realizing automatic feedback and data closed loop.

The invention provides a textile surface defect detection method and system based on image features, and relates to the technical field of textile surface defect detection.The method comprises the steps that an RGB image of a to-be-detected textile is obtained and zoomed to 512 * 512 pixels, and then at least five semantic tags are output through a semantic segmentation model; the method comprises the following steps: adopting hyperspectral imaging with the wavelength of 400-1700 nm and the resolution of 10 nm, calculating the pixel reflectivity through a reflectivity formula, comparing the reflectivity of 5-8 characteristic wavebands, marking as a pollutant if the average deviation exceeds a threshold value, matching the type and position of a stain with a spectral angular distance, and after a standard sample and a semantic region are converted into an LAB color space, extracting a minimum rotation enclosing rectangle and dividing a grid, invalid pixels are filled with 3 * 3 neighborhood effective pixel weighted mean values, and a grid LAB mean value is calculated. A grid pollutant pixel corresponding to an image to be detected is filled with a standard sample in a mean value mode, then channels A and B are clustered through a sliding window, a pilling area is preliminarily judged according to the standard deviation of a channel L, and the pilling area is confirmed by combining the structured light three-dimensional point cloud and comparing with a threshold value.

Disclosed are a display module and a display device capable of minimizing a pressed stain defect while reducing a bezel area. To this end, a thickness of a cushion portion of a cushion plate closer to a bent portion may be reduced, thereby reducing a bending radius of curvature such that the bezel area may be reduced. Further, placing a support layer having a strong supporting force formed of PET into the cushion plate such that the support layer is in contact with the heat-dissipation layer may allow minimizing an unevenness defect on the cushion plate even when a bent pad portion of a display panel presses the cushion portion. Thus, visibility of an internal pressed stain to a user may be minimized.