922 results about "Gray level" patented technology

The invention relates to the technical field of image detection, in particular to a PCBA board defect detection method and system based on image processing, and the method comprises the following steps: carrying out the meshing calculation of a gray scale deviation after a gray scale image is subjected to Gaussian filtering denoising, generating change rate data, carrying out the statistics of a frequency number, constructing a histogram, combining with an Otsu algorithm, and generating a candidate mask; extracting pixels based on a mask, calculating a gradient modulus, screening edge candidate points, carrying out gradient direction connection and morphological processing to generate a complete edge structure, expanding a connected domain through a region growing algorithm, aligning the connected domain with a template contour, and outputting defect coordinates. According to the method, the defect identification sensitivity is improved through combination of gray level image gridding processing and dynamic threshold calculation, a candidate mask is generated through grid gray level change rate statistics and an Otsu algorithm to avoid over-segmentation missing detection, and the contour precision is improved through combination of gradient modulus difference screening and morphological closed operation optimization. The region growing algorithm and template dynamic alignment reduce deformation misjudgment, and staged dimension reduction and feature enhancement reduce calculation complexity and solve resource waste.

The invention belongs to the technical field of fire safety, and discloses a fire-fighting equipment detection early warning method and system based on a visual camera, and the method comprises the following steps: obtaining multi-focal-length image frames of a fixed spray head and a fire extinguisher edge region, judging the continuous change of gray scale in the continuous image frames, and carrying out the detection early warning of the fire-fighting equipment based on the visual camera. Comparing the gray level distribution difference under the long focus and the short focus, identifying the dynamic trajectory of the contour line angular point of the fire-fighting equipment association structure, analyzing the delay of the function action response, marking as an abnormal region, and generating a fire-fighting equipment collaborative early warning information set; according to the invention, by extracting and analyzing the multi-focal-length image frames of the edge areas of important facilities such as a fixed spray head and a fire extinguisher, fine changes of the fire-fighting facilities under differentiated focal lengths can be captured, working states and potential faults of the fire-fighting facilities can be judged, functional abnormalities caused by equipment aging or damage can be recognized in advance, early warning can be carried out in real time, and the safety of the fire-fighting facilities can be improved. And a higher guarantee level is brought to fire safety.

The invention discloses a video stream dynamic fragmentation encryption and block chain evidence storage method, and relates to the technical field of video content security, and the method comprises the steps: calculating a color histogram difference value and an optical flow vector change rate between adjacent frames of an input video, marking the difference value as a scene switching point when the difference value exceeds a preset threshold value, and storing the scene switching point; the method comprises the following steps: preliminarily dividing a video into a plurality of scene segments according to scene switching points, performing content complexity evaluation on the scene segments, calculating gray level co-occurrence matrix characteristics of each frame of image through texture density analysis, calculating edge complexity to extract the number and distribution of Canny edges, and performing motion vector statistics to analyze the size and direction of inter-frame object displacement. The change rate between adjacent pixels in the color space is measured according to the color change gradient; the video stream dynamic fragment encryption and block chain evidence storage method is suitable for video contents of different types and complexities, and has relatively high detection accuracy and robustness.

The invention relates to the technical field of image analysis, in particular to an ETFE film cutting control method and system based on image recognition, and the method comprises the following steps: enhancing an edge through multi-scale Gaussian filtering, extracting a gray level change rate pixel by a Sobel operator, generating a path segment, outputting a coordinate, calculating a gradient direction, constructing a direction continuous sequence, and recording an angle deviation; a dynamic window judges abnormal fluctuation to trigger calibration, a fluctuation segment vector is replaced to update a motion track, and a B spline is fitted to generate a track output cutting instruction. According to the method, multi-scale Gaussian filtering is combined with Sobel operator enhanced edge extraction continuous pixels, the fracture problem caused by a fixed threshold value is solved, vector space projection constructs a direction sequence to quantify angle deviation, dynamic window range judges fluctuation to trigger calibration, and a vector replacement strategy corrects an abnormal section control point direction. A smooth track is generated through B spline interpolation, mechanical vibration is reduced, and the flexible material cutting precision and the equipment cooperation efficiency are improved.

The invention relates to the technical field of optical detection, in particular to an AOI automatic optical detector calibration method and system, and the method comprises the following steps: obtaining a standard color palette RGB reflection reference, collecting a measured value, calculating an offset contrast, extracting image brightness, generating a correction ratio, converting gray level, extracting abrupt change, screening anomalies, and analyzing a brightness trend to generate a finishing record. And formulating a rule matching correction output comparison table. According to the method, the accuracy and reliability of image detection are enhanced through detailed analysis and accurate correction of color offset and brightness difference, the scheme combines real-time light source data and image information, fine changes are effectively identified, the misjudgment rate is reduced, the accuracy of gray and color correction is improved through comprehensive analysis of light intensity offset and regional brightness trend, and the accuracy of image detection is improved. The detection efficiency and quality of a high-density circuit are ensured, the detailed calibration mechanism remarkably reduces detection errors especially in a high-precision environment, and product quality control and the stability of a production process are optimized.

The invention discloses a hydraulic engineering dam body crack detection method and system based on machine vision, and relates to the technical field of computer vision. A high-definition camera is used for shooting a dam body image, obtaining a sample data set, extracting feature parameters of different noise types, and constructing a feature database; filtering the current dam body image to obtain a first dam body image, enhancing the crack gray scale difference of the first dam body image, obtaining a second dam body image, segmenting the second dam body image, screening candidate cracks in the crack image, and identifying real cracks according to the edge features of the candidate cracks. According to the method, a feature database is constructed, a targeted dam body image processing flow is combined, a high-definition camera collection and automatic processing flow is used, filtering parameters are dynamically adjusted by constructing the feature database, gray level enhancement and precise segmentation are combined, real cracks are precisely recognized through edge feature analysis and comparison, and quantitative parameters are output; and a reliable basis is provided for dam body safety assessment.

The invention relates to the technical field of image recognition, in particular to a bearing surface defect automatic detection system based on image recognition, and the system comprises an image collection and preprocessing module which obtains a bearing surface image, and carries out the cutting and filtering of the image, so as to remove the noise and adjust the contrast, and obtain a processed image; and carrying out gray level conversion on the processed image to generate a preprocessing result. According to the method, environmental noise interference is reduced through image cutting and filtering, the contrast ratio is adjusted, the image quality is optimized, and the distinction degree of the defect area and the background is improved. Image channel information is standardized through gray level conversion, so that the calculation precision of feature extraction is kept consistent, and the analysis result is prevented from being interfered by multi-channel data. According to the method, image hierarchies with different scales are constructed by an image pyramid method, so that the adaptability to fine cracks and large-area spalling defects is improved in the local contrast and texture feature extraction process, and the stable recognition capability to defects with different sizes is enhanced.

The invention relates to the technical field of image state recognition, in particular to an unhooking and rehooking AI accurate recognition grabbing system and method based on pattern recognition, in the system, node construction is conducted through contour changes, boundary difference values and gray level dynamic states of a hook assembly in an image sequence frame, edge displacement accumulation analysis is combined, meanwhile, through a graph neural network, an image sequence frame is obtained, and the image sequence frame is obtained. Cosine values and coordinate difference values between nodes are subjected to combined comparison, and a path hopping sequence is constructed, so that the response sensitivity to state abrupt change is enhanced, a key path of morphological evolution can still be stably extracted under the condition of complex background interference or local shielding, the anti-interference performance and fault tolerance of space path identification are effectively improved, and the space path identification accuracy is improved. Statistical modeling is further carried out on state rate sudden change points through a hidden Markov model, paragraph merging and invalid fragment removing operation are carried out on abnormal point segments by matching a standard state mode, a state label sequence is constructed, and accurate division of high-confidence and multi-segment continuous states is achieved.

The invention discloses a geographic information collection method and system based on a remote sensing image, and relates to the technical field of geographic information extraction, and the method comprises the steps: firstly obtaining a remote sensing image sequence, carrying out the time sequence registration, and extracting pixel gray level change features to generate building edge region pixels; delimiting an edge area, and identifying disturbance points; road node positions are extracted, and a path steering angle is corrected; establishing a disturbance chain communication density value; and finally generating a structured image data unit. According to the method, pixel gray level change features are extracted through time sequence registration, a building edge area is positioned, disturbance points are recognized by combining the disturbance amplitude and gray level direction consistency, path steering deviation is corrected, and the stability of a path communication structure is enhanced; a disturbance chain communication density value is established through an extension path, a continuous response boundary is extracted by fusing a gray scale trend and structural integrity, an image data unit with a gray scale trend, spatial connectivity and disturbance characteristics is generated, and the accuracy and continuity of boundary recognition in a complex region are improved.

The invention discloses a projection single attitude calibration method, system and device and a medium, and relates to the technical field of computer vision and projection interaction.The method comprises the steps that a plurality of calibration patterns are generated and projected to a calibration plane, gray level images are collected, corner point detection and sub-pixel refinement processing are carried out, and a projection single attitude calibration result is obtained. The method comprises the following steps: acquiring an angular point corresponding relation between a projector image and a camera image, carrying out iterative solution on a projection matrix of a projector and a projection matrix of a camera, separating an internal parameter matrix and an external parameter matrix, calculating a basic matrix and an essential matrix, and decomposing to obtain a rotation matrix and a translation vector of the camera relative to the projector; camera imaging distortion is estimated, a distortion correction coefficient is obtained, and distortion correction is carried out; and counting a re-projection error generated in the correction process, and adjusting parameters of the projector and the camera until the error meets a preset precision requirement. Projector parameter calculation is completed through projection single posture calibration, posture adjustment is not needed in the calibration process, the automation degree is improved, and meanwhile manual intervention is reduced.

The invention relates to the technical field of image analysis, in particular to a mobile phone screen uniformity detection method based on image analysis, which comprises the following steps: extracting a central region gray sequence, calculating a slope to generate a trend chart, positioning a change starting point to adjust coordinates, extracting gray difference to enhance comparison, and analyzing a bright-dark span to generate a map. And extracting a gravity center gray level judgment difference and outputting a detection scheme. According to the method, through multi-direction gray scale sequence slope extraction and fluctuation aggregation feature construction, detail capture of a brightness trend is enhanced, reverse gray scale change starting point positioning and coordinate offset are combined, abnormal region positioning precision is improved, a local transition zone and a jump section are extracted, and gray scale fluctuation partition enhancement is realized. Through high-gray pixel set gravity center and local fluctuation difference analysis, brightness abnormal points are accurately recognized, the whole process is fused with the links of gray trend extraction, change track adjustment, contrast enhancement, gravity center analysis and the like, the detection sensitivity and the judgment precision are enhanced, and the screen brightness differential detection requirement is met.

The invention relates to the field of infrared image processing, in particular to an infrared blind pixel detection method based on temperature self-adaption and time-frequency domain synthesis, which comprises the following steps of: traversing an infrared image by using a sliding window to obtain gray values of pixel points around a current pixel point; carrying out image edge judgment by using a Sobel-Otsu algorithm based on working temperature dynamic adjustment, and reserving a non-image edge window to participate in blind pixel judgment; grouping pixel points in the window according to horizontal and vertical directions; calculating the alpha filtering value of the gray scale of each group of pixels; calculating the deviation between the gray value of each pixel in the window and the alpha filtering value of the corresponding row coordinate and column coordinate group; if the deviation is greater than an adaptive threshold, setting the blind pixel as a potential blind pixel, and constructing a potential blind pixel distribution matrix; a time domain accumulation method for pre-screening periodic noise based on FFT is adopted, periodic noise interference is eliminated through frequency domain analysis, and then high-probability blind elements are screened through time domain accumulation; and obtaining a final blind pixel detection result. The method can effectively cope with the working temperature change of the sensor and the noise interference in a complex environment, and remarkably improves the accuracy and anti-interference capability of blind pixel detection.

The invention discloses an SAR satellite remote sensing surface water extraction method for an African climate complex region, and the method comprises the steps: carrying out the preprocessing of satellite image data, and making sample point label data; making a shadow mask file, and removing the mountain shadow on the satellite image data; calculating a gray level co-occurrence matrix to generate texture features; establishing a multi-dimensional feature space, and screening the feature space; building a semi-supervised cooperative training model by using two random forest classifiers, and introducing label-free data to assist in model training; and predicting the global surface water distribution of the research area. According to the method, the SAR remote sensing image is combined with the semi-supervised collaborative random forest model, the manpower and time cost of actual sample labeling is greatly reduced, the surface water monitoring efficiency is improved, the problem of visible light remote sensing data missing in regions with complex climates can be solved by using microwave remote sensing data, and the method is suitable for popularization and application. And timely and accurate information is provided for water resource management in the African region.

The invention relates to the technical field of image segmentation, in particular to a medical image processing method for tumor partition recognition, which comprises the following steps: acquiring a multi-modal medical image, extracting gray features and adjusting image gray, recognizing tissue boundary and metabolism features, grouping and generating tumor boundary response partitions, extracting heterogeneous nodes and classifying and labeling. Dividing a core area, an infiltration area and a necrosis area, and generating a tumor functional structure chart. According to the method, the gray frequency and the edge trend of the multi-modal image are unified, the structure contrast is enhanced, the consistency of each modal image in space and gray level is ensured, the information fusion integrity is improved, the gray, texture and metabolism characteristics are combined, the density mutation and metabolism enhancement region is identified, and the boundary clear contour is constructed; boundary partitions are established through response direction consistency, the cross-modal discrimination ability is enhanced, texture and metabolic characteristics are fused to classify heterogeneous nodes, a core area, an infiltration area and a necrosis area are divided, and functional partitions with clear expression and clear structures are generated.

The invention relates to the technical field of image processing, in particular to an intelligent welding defect positioning and detecting system based on image processing. Obtaining a suspected noise degree according to gradient features and neighborhood gray level distribution features of pixel points in the welding image; obtaining a gray confidence coefficient according to the gray features of the pixel points and the gray difference features of the pixel points and the neighborhood; obtaining a structure confidence coefficient according to the area feature of the connected domain where the pixel point is located, the contour change feature of the edge line of the connected domain and the gradient distribution feature of the edge line in the normal direction; obtaining a denoising coefficient of the pixel point according to the suspected noise degree, the gray level confidence coefficient and the structure confidence coefficient; and adjusting the standard deviation in the Gaussian filtering algorithm according to the de-noising coefficient. The method comprises the following steps: denoising a welding image according to an adaptive standard deviation, and performing image enhancement on the denoised welding image to obtain a to-be-detected image; welding flaw detection is carried out on the to-be-detected image, and the detection accuracy is improved.

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 conductive powder mixing uniformity detection, and discloses a conductive powder mixing uniformity detection method, which comprises the following steps: acquiring a conductive powder image, and converting the conductive powder image into a grayscale image; setting a gray level according to the gray value of each powder before mixing, and carrying out gray level conversion on the gray image; gray level co-occurrence matrixes of the converted gray level images under different interval parameters and differences between the gray level co-occurrence matrixes under different intervals are calculated; and judging whether the conductive powder is uniformly mixed or not according to the difference between the gray-level co-occurrence matrixes. By adopting the scheme of the invention, the mixing uniformity of the powder can be accurately judged according to the difference between the conductive powder textures obtained under different parameters.

The invention relates to the technical field of size measurement, in particular to a clock part size measurement method and system based on machine vision, and the method comprises the following steps: based on clock part surface data, analyzing reflection characteristics and pixel statistics, combining a light source angle to optimize imaging, comparing gray level distribution and shadow area, and screening an image with optimal marginal definition. And fusing the brightness and contour information, screening multi-direction size data, judging the stability of boundary pixels, and outputting a size measurement result. According to the method, multiple data features are automatically collected and fused in the collection process, parameter optimization of different surface structure information is combined, cross validation of multi-direction size information is synchronously completed in the measurement link, abnormal fluctuation data are eliminated, continuous judgment and result screening are achieved in the whole processing flow, and the accuracy of data processing is improved. The stable size data of batch parts can be efficiently obtained under the automatic discrimination process, the influence of abnormal interference on the output accuracy is reduced, and the uniformity and traceability of the measurement data are improved.

The invention discloses a weldment surface defect detection method and system based on a gray level co-occurrence matrix and YOLOv11, and belongs to the technical field of weldment surface defect detection. According to the technical scheme, the method and system for detecting the surface defects of the welding part based on the gray-level co-occurrence matrix and the YOLOv11 specifically comprise the following steps that S1, an industrial camera collects surface images of the welding part under different defect types and illumination conditions, data enhancement processing is carried out, and data enhancement comprises horizontal overturning, color changing, scaling and noise injection; s2, marking bounding box positions and category labels of defects in the enhanced image; according to the method, the texture features of the gray level co-occurrence matrix and the depth features of the improved YOLOv11 model are fused, the attention mechanism and the multi-scale feature fusion technology are combined, the problems that a traditional method is low in efficiency and poor in adaptability and a deep learning model is insufficient in small target detection capacity are effectively solved, and the method has the advantages of being high in detection precision and high in environmental adaptability.

The invention relates to the technical field of crop disease and insect pest image recognition, and particularly discloses a crop disease and insect pest image recognition method based on a large model, and the method comprises the steps: obtaining a multi-angle leaf image through high-resolution imaging equipment under a controllable illumination condition, and obtaining a target image in a unified format; extracting scab texture complexity features in combination with a local binary pattern and a gray-level co-occurrence matrix algorithm, and performing multi-channel statistical analysis on RGB and HSV color spaces to generate color heterogeneity feature vectors; further fusing the two types of features into a composite disease feature vector, inputting the composite disease feature vector into a probability model constructed based on a support vector machine and a Monte Carlo Dropout mechanism, and outputting probability distribution and confidence score of disease and pest categories; and dynamically adjusting a model training strategy according to a confidence level, triggering a feedback mechanism for a low-confidence sample, generating a synthetic image by using a conditional generative adversarial network, and optimizing model parameters in combination with incremental learning to realize stable identification modeling of rare or complex disease types.

The invention relates to the technical field of size detection, in particular to a component size detection system based on machine vision, which comprises an interference area identification module, an edge repair module, a structure clustering module, a distortion analysis module and an attitude restoration module. According to the method, the gray level change trend of each pixel and the adjacent points in the image is obtained, the high-reflection suspected interference area is identified, the edge response cancellation processing is executed, and linear interpolation compensation is carried out by combining the effective edge response values of the adjacent areas; after a continuous contour trend is constructed, clustering and sub-grouping division are carried out according to an included angle difference trend between contour geometric vectors, a three-axis direction response linear array variation amplitude is extracted to construct a distortion curvature mapping table, existence of non-equal-interval deformation is judged, reverse angle compensation is applied, and a three-axis intersection point convergence process is completed through a nonlinear optimization algorithm; and according to the three-dimensional coordinates, calculating a geometric dimension and outputting a detection result, thereby realizing self-adaptive detection of different postures and surface interference component dimensions.

The invention relates to the technical field of flour quality detection, and discloses a flour quality detection method and system based on machine vision, and the method comprises the steps: obtaining an original multi-view image of a flour storage bin, and obtaining an initial image data set; performing standardization processing to obtain a standard image data set; extracting features such as gray level to construct an initial spatial distribution feature map; enhancing the abnormal region to obtain local residue detail information; classifying the residue type and the pollution degree to obtain a classification result; fusing the data to construct a three-dimensional residue distribution map; analyzing the atlas to obtain detailed report data; and generating a high-risk area visual distribution map to obtain a spatialization evaluation result. According to the method, accurate detection and quantitative evaluation of the residues in the storage bin can be realized, and the flour quality control efficiency is improved.

The invention provides a fatigue test method, system and equipment based on texture change of carbon fibers and a medium, and belongs to the technical field of material tests.The method comprises the steps that a carbon fiber composite material is selected as a test sample; applying a fatigue load to the test sample according to a designed test condition, and collecting a surface image of the test sample; the method comprises the following steps of: preprocessing an acquired image, clustering according to the gray level and texture similarity of pixels to obtain a plurality of clustering subareas, and marking different clustering areas by using different colors to complete color mapping; extracting texture feature parameters from the image after color mapping, and performing PCA dimension reduction to obtain key texture feature parameters; and comparing the key texture feature parameters of the image under different test conditions, identifying texture feature changes, and completing fatigue performance evaluation. According to the method, image processing and texture feature analysis are combined, so that non-destructive testing of the CFRP fatigue performance is realized, and the testing accuracy and efficiency are improved.

The invention relates to the technical field of medical image auxiliary diagnosis, in particular to a nuclear magnetic resonance image auxiliary diagnosis method and system based on multi-modal fusion. The method comprises the following steps: step 1, synchronously acquiring a three-dimensional T1 weighted structure image, a T2 weighted fluid attenuation inversion recovery image and diffusion weighted imaging data of a subject, carrying out spatial registration by taking the T1 weighted image as a reference, and executing skull stripping and gray scale standardization; 2, individualized brain region segmentation is carried out based on a brain anatomical map, the lesion sensitivity weight of each modal is calculated for each segmented brain region, and the weight is obtained by quantifying the following parameters; step 3, extracting multi-modal image features in each brain region; and 4, inputting the fusion features of the whole brain region into a multi-task classifier. The standardization and alignment of the multi-mode MRI image in the space and gray level are realized, and the problems of space mismatch and feature interference among different modes are effectively solved.

The invention relates to the technical field of image analysis, in particular to a vegetation fine classification and recognition method and system based on an unmanned aerial vehicle, and the method comprises the following steps: obtaining a multispectral image through the unmanned aerial vehicle, extracting red edge reflectivity, NDVI and gray-level co-occurrence contrast, generating a feature vector in a standardized manner, calculating neighborhood offset to obtain a dynamic weight, and combining the dynamic weight into a weighted vector; high discrete features are screened as effective channels, multi-scale clustering is carried out, center and region growth extension recognition is optimized, and a vegetation classification atlas is generated. According to the method, a neighborhood pixel feature offset dynamic weight mechanism is introduced, multi-spectral feature dimension contribution degree is adjusted in a self-matching mode, effective channels are screened based on full-image dispersion, redundant interference is eliminated, image pyramid multi-scale clustering and consistency constraint are fused, the complex vegetation boundary recognition capability is improved, dynamic weight and multi-scale optimization are coordinated, and the method is high in robustness and high in robustness. Sample dependence is reduced, and accurate distinguishing of spectrum similar vegetation is achieved.

The invention relates to the technical field of image analysis, in particular to a method for monitoring the bottom of a battery swap station vehicle based on AI vision, which comprises the following steps: acquiring an image frame to extract bottom gray level distribution, calculating illumination difference to judge an abnormal frame, extracting a gray level jump band, calculating a slope, screening miscellaneous points, identifying a closed contour, and extracting a texture direction and a pitch. And analyzing an offset screening abnormal number, calculating an included angle and a contraction mark mutation pixel, and generating a bottom monitoring result. According to the method, the image stability under the complex illumination condition is enhanced by analyzing the gray level distribution and illumination change in the image frame, the edge response density difference elimination mechanism is combined, the structure overlapping interference is avoided, the closure degree of the closed structure and the texture period are extracted, and the structural integrity recognition is enhanced; texture direction and pitch cross-frame change analysis supports micro structure offset early warning, boundary point included angles are combined with contour shrinkage trends, accurate marking of abnormal areas is achieved, and sensitivity, accuracy and reliability of bottom monitoring are improved.

The invention relates to the technical field of image analysis, in particular to a metal composite material surface defect intelligent detection method based on machine vision, which comprises the following steps: acquiring an original image and converting the original image into an observation matrix; decomposing the matrix into a low-rank matrix and a sparse matrix through a robust principal component analysis algorithm; utilizing a nuclear norm constraint low-rank matrix to establish a background statistical model, and utilizing a norm constraint sparse matrix; positioning a defect area in the sparse matrix and extracting a gray level co-occurrence matrix feature vector; performing connected domain analysis on the sparse matrix, calculating geometrical morphology parameters and evaluating a stress concentration coefficient; and establishing a self-adaptive decision model to carry out fusion judgment so as to judge the physical damage. According to the method, through low-rank sparse matrix decoupling and multi-dimensional statistical geometric feature fusion analysis, accurate stripping of weak defects and quantitative evaluation of physical damage attributes under a complex background are realized.

The invention discloses a stem cell fusion degree detection method and system based on artificial intelligence, and a storage medium. The method comprises the following steps: carrying out image preprocessing on a cell microscope image; automatically calculating an optimal threshold value by using an image threshold value segmentation algorithm to obtain a cytoplasm mask; median filtering is carried out on the original image to reduce noise, then an adaptive threshold segmentation method is adopted, a local threshold is calculated according to local area gray level distribution, and a cell nucleus binary image is generated; performing connected region marking on the cell nucleus binary image, calculating the area attribute of each region, and performing filtering according to a cell nucleus removal ratio parameter to obtain a cell nucleus mask; performing logic OR operation on the cytoplasm mask and the cell nucleus mask to obtain a complete cell segmentation result; and calculating the fusion degree of the stem cells based on the cell segmentation result. Therefore, the problems of accuracy and consistency of judging the fusion degree of the stem cells by observing microscope images with human eyes in the prior art are solved, and accurate detection of the fusion degree of the stem cells is realized.

The present invention discloses a driving method and a pixel circuit for a pixel circuit. The pixel circuit includes at least two driving transistors connected in parallel, and the aspect ratios of the at least two driving transistors are different; the driving method of the pixel circuit includes: obtaining the maximum display brightness value of a preset area in the to-be-displayed picture; determining whether the maximum display brightness value of the preset area is greater than a first preset value; if the maximum display brightness value of the preset area is greater than the first preset value, when the to-be-displayed gray level in the preset area is less than a preset gray level, controlling the driving transistor with the smallest aspect ratio in the pixel circuit corresponding to the preset area to drive; if the maximum display brightness value of the preset area is less than or equal to the first preset value, controlling the driving transistor with the smallest aspect ratio in the pixel circuit corresponding to the preset area to drive. The present invention can improve the mura phenomenon.

The invention relates to the technical field of image detection, in particular to a battery pack injection mold production defect detection and identification method. Obtaining a first suspected defect degree according to gray level distribution characteristics in a preset neighborhood window of edge pixel points in the appearance image and change characteristics of the edge lines; obtaining a second suspected defect degree according to a texture distribution feature and a texture strength feature in a preset neighborhood window of the edge pixel point, and a gray difference feature and a gradient difference feature between the preset neighborhood window and a preset neighborhood large window; and obtaining an enhancement coefficient of the edge pixel point according to the first suspected defect degree and the second suspected defect degree. According to the invention, the preset sharpening intensity is adjusted according to the enhancement coefficient and the gradient features of the edge pixel points, the appearance image is sharpened and enhanced according to the adaptive sharpening intensity, and the injection mold defect is analyzed according to the sharpened and enhanced appearance image, so that the accuracy of defect identification is improved.