2635 results about "Gradation" patented technology

The invention provides an intelligent visual management method and system for enterprise big data. The method comprises the following steps: extracting a space-time association rule of an operation and maintenance report fault field and an equipment log error code, and generating a dynamic mapping data stream to drive a three-dimensional visual association topology; the method comprises the following steps: collecting cabinet vibration energy data, and synchronizing a highlight energy sudden increase area and error log entries according to a timestamp; dynamically distributing a vibration energy weight, and generating a risk probability matrix in combination with an error increment; fusing the time sequence characteristics and the physical topology path, constructing an abnormal event timestamp graph, and marking a fault propagation chain; and based on the map density gradient and the causal association strength, superimposing and rendering the penetrating thermodynamic diagram, associating topology, a fault chain and an equipment structure, and adaptively adjusting the color gradation highlighting abnormal region. According to the technical scheme provided by the invention, the multi-source fault correlation analysis efficiency is improved, and the abnormal risk is dynamically, visually and accurately positioned.

The invention relates to the technical field of image analysis, in particular to a heat exchanger welding quality visual inspection method and system, and the method comprises the following steps: collecting original image data from a welding image, carrying out the preprocessing of the original image data, including noise removal and brightness adjustment, and obtaining a preprocessed image; according to the method, in the whole process from welding image data collection to defect recognition result generation, preprocessing operation based on high-frequency noise pixel filtering and gray value frequency adjustment is carried out on original image data, interference information in an image is effectively reduced, and the reliability of the image data is improved; meanwhile, continuous iteration deconvolution operation and pixel detail optimization are carried out on the basis of preprocessing, the distinguishing precision and texture detail discernibility of the welding image are improved, the discernibility of the welding defect under different scales is enhanced through multi-scale-level image feature decomposition, and the precision of image feature data is comprehensively improved.

The invention discloses a cut workpiece defect detection method and system based on image feature feedback, and relates to the technical field of image processing.The method comprises the steps that cut workpiece technological characteristics are obtained, a preset defect type library is constructed, a hardware system is built, and parameters are initialized; synchronously acquiring a multi-view original image, and storing and associating annotation information; de-noising the original image, enhancing the contrast, and extracting a region of interest ROI; extracting texture, shape, edge and gray features from the ROI, and screening through a Relief-F algorithm to obtain an optimal feature subset; inputting into an SVM (Support Vector Machine) model for reasoning, and screening to obtain an effective defect detection result; and calculating an evaluation index and generating a feedback signal, and performing iterative optimization after adjusting parameters. The system comprises an acquisition module, a master control module, a data processing module and a display module. Through the precise design and closed-loop feedback of the whole process, the precision, efficiency and long-term adaptability of defect detection of the complex cutting workpiece are improved, and the industrial quality management and control requirements are met.

The invention relates to the technical field of image processing, in particular to a machine vision-based cover film surface defect detection method, which comprises the following steps of: firstly acquiring a cover film surface image, iteratively updating a segmentation threshold value for multiple times according to an Otsu algorithm, segmenting a scratch and a normal area by using the current threshold value in each iteration updating process, clustering pixel points of the scratch area, and obtaining a target image; a scratch segmentation coefficient is calculated according to the difference between a pixel point gray average value of each cluster and a standard gray value and the average Euclidean distance from the pixel point to the fitting curve, a current threshold segmentation effect quantized value is calculated by combining the difference between a scratch area and a normal area, and after iteration is completed, a threshold corresponding to the maximum quantized value is selected as the optimal threshold. And segmenting the image by using the optimal threshold value to determine a scratch defect detection result. According to the method, the process of calculating the segmentation threshold is optimized, the noise interference is reduced, the segmentation requirements of defects such as scratches are better met, the segmentation accuracy is improved, and the scratch defects on the surface of the cover film can be effectively identified.

The invention discloses a method for identifying impurities and bubbles in a wine bottle based on multi-feature comprehensive judgment, and belongs to the technical field of image processing and computer vision, and the method comprises the steps: employing a high-resolution industrial camera to be combined with an LED light source, and continuously shooting multiple frames of images of the wine bottle under the consistent shooting angle and illumination condition; carrying out the preprocessing of the collected image through employing a self-adaptive histogram equalization and Gaussian blur method; extracting the edges of impurities and bubbles in the wine bottle on the image by adopting a Canny edge detection algorithm to obtain an edge image; segmenting the edge image into a plurality of target areas by using a contour detection technology, and extracting possible bubble and impurity areas from the edge image; extracting target area features including circularity, gray average, gray uniformity, texture features and color space; and according to the extracted features, bubbles and impurities are distinguished by applying a comprehensive judgment rule. According to the method, the bubbles and the impurities are comprehensively judged by adopting multi-feature combination, so that the accuracy and the robustness of detection are greatly improved.

The invention discloses an intelligent positioning method and system for can making stamping, and relates to the technical field of image processing. Comprising the following steps: acquiring a tank stamping part image, and segmenting the tank stamping part from a background by adopting a self-adaptive threshold segmentation algorithm to obtain segmented tank region image data; utilizing a moment-based sub-pixel edge detection method to accurately position the edge coordinates of the contour of the tank body, and obtaining the key dimension parameters of the tank body; extracting texture features of the surface of the tank body by adopting a gray-level co-occurrence matrix and a local binary pattern algorithm, and judging whether the texture features belong to surface defects or not through a support vector machine classification model; the method solves the problem that edge information is difficult to accurately extract when a traditional image processing method faces tank surface reflection and a complex background, ensures the accuracy of a measurement result by establishing a tank coordinate system and performing attitude correction, overcomes the problem that the tank is easy to deviate or incline in the production process, and improves the production efficiency. And high-efficiency and accurate quality detection of the tank stamping part is realized.

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 recognition, in particular to an image monitoring system for traditional village heritage risk assessment, and the system comprises a heritage image collection module which is used for obtaining an image data stream of a target traditional village building surface, carrying out the geometric correction of original heritage image data, carrying out the image brightness equalization processing, and obtaining an image data stream of the target traditional village building surface; and establishing a calibrated image set. According to the method, image distortion and local overexposure caused by shooting angle difference or uneven illumination on the surface of a traditional village building are eliminated through geometric correction and brightness equalization processing, and the input data quality of subsequent feature analysis is improved. The consistency degree of crack textures in a neighborhood range is quantified based on gray gradient direction field data, the gray contrast of continuous crack edges is enhanced in combination with a dynamic threshold adjustment mechanism, non-structural texture interference is inhibited, and separability of micro cracks and background materials is enhanced.

The invention discloses a monocular vision inertial positioning method based on fine pre-integration and adaptive vision inertial weight, which comprises the following steps of: firstly, constructing key frames by using parallax by adopting a sliding window method, and processing the output of an inertial measurement unit between adjacent key frames by using fine pre-integration; initializing a visual inertial odometer to obtain external parameters and internal parameters of an initial sensor and scale and speed information of the odometer; normalizing the acceleration and the angular velocity of each pre-integration quantity by adopting an L2 norm, and weighting the components of the covariance matrix constrained by each fine pre-integration by taking the normalized acceleration and angular velocity as adaptive inertia optimization weights; based on the quality of feature matching after optical flow tracking, a visual weight is obtained according to the number of times of feature continuous tracking and changes of gray values before and after the feature continuous tracking, and the size of an information matrix of a visual re-projection residual error is adjusted in a self-adaptive mode; and finally, performing optimization iteration on the camera pose by adopting a sliding window and a factor graph to minimize a residual term based on mahalanobis distance measurement and obtain a final positioning result.

The invention relates to the technical field of image enhancement, in particular to a protector bimetallic strip detection method based on machine vision, and the method comprises the steps: obtaining a surface image of a bimetallic strip, and carrying out the preprocessing of the surface image, and obtaining a gray image; performing threshold segmentation on the grayscale image to obtain at least one segmentation region; obtaining an evaluation coefficient of each segmented region, obtaining an adaptive cutting parameter when contrast-limited adaptive histogram equalization is carried out on each segmented region according to the evaluation coefficient of each segmented region, and carrying out image enhancement on each segmented region in the grayscale image according to the adaptive cutting parameter to obtain an enhanced grayscale image; the corrosion detection result in the bimetallic strip is obtained by using the enhanced gray level image, so that the enhanced image can reflect more effective information, and the surface corrosion detection precision of the bimetallic strip according to the enhanced image is improved.

The invention relates to the technical field of image data processing, in particular to a forge piece surface defect detection method and system based on machine vision. The method comprises the steps of obtaining a gray image of a to-be-detected forging surface; determining a boundary significance weight; determining a path consistency weight; screening the boundary significance weight and the path consistency weight to obtain a final weight; and carrying out local adaptive threshold segmentation on the final feature map to obtain a binary image, and carrying out defect identification on the surface of the forge piece to be detected based on a connected region in the binary image. According to the method, the boundary significance weight and the path consistency weight are constructed and are respectively used for accurately positioning defect edges and verifying structure continuity, texture interference is effectively inhibited, and false alarms are reduced; through adaptive Gabor filtering, the problem of a response blind area of a traditional method is solved, finally two weights are fused to modulate filtering response, and the accuracy of forging surface defect detection is improved.

The invention relates to the technical field of metal defect detection, and discloses an aluminum alloy round aluminum rod surface defect image super-resolution method. The method comprises the following steps: acquiring a low-resolution original image sequence of surface defects of the aluminum alloy round aluminum rod, and synchronously acquiring gray value distribution at different illumination angles through a multi-channel optical sensor; constructing a dynamic degradation model according to pixel displacement of adjacent frames in the original image sequence, extracting cross-scale defect features in the original image sequence, and taking output parameters of the dynamic degradation model as spatial constraint conditions of a feature extraction network; and a high-resolution defect image is generated through the multi-stage residual error reconstruction network, the high-resolution image output by the reconstruction network is fed back to the dynamic degradation model, and the frequency domain response coefficient of the spatial fuzzy kernel function is updated to form closed-loop optimization. The identification degree of defect features is improved, and a reliable image data basis is provided for accurate detection of the surface defects of the aluminum alloy round aluminum rod.

The invention discloses a method and a system for improving Mura defect classification precision, electronic equipment and a storage medium, which are used for realizing accurate classification of Mura defects of low-gray-scale images. The method for improving the Mura defect classification precision comprises the steps of obtaining an image on a display screen to be detected; performing texture filtering processing on the image to obtain a filtered image; performing graying processing and defect segmentation processing on the filtered image to obtain a to-be-detected image; inputting the to-be-detected image into the Mura classification model to obtain a classification result; wherein the Mura classification model is obtained through the following training method: obtaining first data collected by a plurality of sensors; preprocessing the first data to obtain second data; extracting feature data in the second data; fusing the feature data according to an orthogonal sequence fusion algorithm to obtain a fusion result; and training according to a fusion result to obtain a Mura classification model.

The invention relates to the field of data processing, in particular to a part machining detection method and system, and the method comprises the steps: obtaining images of a to-be-detected part and a template part on a production line, and carrying out the preprocessing of the images, and obtaining corresponding gray images; using an edge detection algorithm to obtain a gradient magnitude and a gradient direction of each pixel point in the grayscale image, performing non-maximum suppression in a neighborhood of each pixel point, screening candidate feature points, and based on the candidate feature points and a plurality of attributes in the neighborhood, respectively obtaining significance feature values of the candidate feature points of the to-be-detected part and the template part; matching is carried out according to the saliency characteristic values of the to-be-detected part and the template part, the comprehensive matching degree is calculated, whether the image of the to-be-detected part is abnormal or not is judged based on the comprehensive matching degree, and part machining detection is completed; according to the method, the real feature points and the false feature points are effectively distinguished by introducing multi-dimensional features such as significance feature values and shape complexity, and the mismatching rate caused by similar texture structures is reduced.

The invention relates to the technical field of image processing, in particular to a bridge crack calibration method and device based on computer vision, and the method comprises the steps: collecting a grayscale image of a bridge, and extracting all edge contours in the grayscale image; obtaining each suspected edge contour; extracting a skeleton line of each edge contour, and determining a first evaluation value of each suspected edge contour; the irregularity and the edge collaboration degree of each suspected edge contour are calculated, and a second evaluation value of each suspected edge contour is determined; and determining a discrimination coefficient of each suspected edge contour, evaluating each suspected edge contour, and calibrating the crack of the bridge. According to the invention, the detection error of other interferences on the bridge crack can be reduced, the accuracy of detecting the crack on the bridge is improved, and the bridge crack is calibrated more accurately, so that the safe use of the bridge is ensured.

The invention relates to the technical field of computer vision, in particular to a defect segmentation positioning method and system for an inorganic mineral casting image, and the method comprises the following steps: calling an illumination image to analyze brightness, matching exposure parameters, splicing the image, analyzing a gradient, recognizing a defect, screening an effective region, calculating a gray variance, and constructing roughness weight recognition texture features. According to the method, the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined, the exposure interval can be dynamically adjusted when the casting image is processed, the defect type information is output in the direction, and the positioning information is generated by correcting the recognition position in combination with the actual coordinate of the target spot. The method has the advantages that the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined; the method has the advantages that the method is simple and easy to implement, detail loss of overexposure areas is reduced, the recognition precision of defect areas is improved, accurate area segmentation and classification processing are achieved, roughness weight calculation combining gray variance and pixel density is combined, the sensitivity to surface fine defects is enhanced, and the precision and reliability of defect positioning are improved.

The invention discloses a welding quality visual detection method, and relates to the technical field of weld defect detection, and the method comprises the steps: obtaining a pixel neighborhood block corresponding to each pixel point in a to-be-detected welding gray image, and obtaining the illumination influence degree in each pixel neighborhood block according to the gray feature and gradient feature of each pixel point in each pixel neighborhood block; according to the position distribution characteristics between the edge end points of the adjacent edges in the welding gray level image to be detected, the same kind of edges are judged; obtaining a gamma value adjustment coefficient of a pixel point at the central position in each pixel neighborhood block by obtaining the confidence coefficient that each similar edge is the inner edge of the welding seam and the defect degree of each similar edge; the gamma value of each pixel point is adjusted, the gray level output value of each pixel point in each pixel neighborhood block is obtained, and an optimized welding gray level image is obtained; performing quality detection on the to-be-optimized welding grayscale image; the accuracy of welding quality detection can be improved.

The invention discloses a quality detection system and method for a glue injection laser contour, and belongs to the technical field of quality detection, and the method comprises the steps: obtaining a reflection point cloud signal of a glue injection finished product, constructing a point cloud data stream, setting an image reconstruction method, constructing a glue layer three-dimensional model, synchronously generating a pseudo-color grey-scale map, and achieving the visual representation of the surface texture of a glue layer. A three-layer feature extractor is constructed, a quality primary judgment method is set, features of different scales are fused in a cross-layer mode, the problem of missing detection of complex defects in traditional single-feature detection is solved, basic defects are automatically recognized, and a quality primary judgment result is output; and performing defect judgment on the glue-injected finished product which is unqualified in primary judgment, setting a defect fine judgment method, performing voxel-level segmentation on the three-dimensional image, calculating defect characteristics, and judging whether the defects affect the use performance of the product or not so as to generate a fine judgment result, so that accurate positioning of complex defects is realized, and the defect type identification accuracy is improved.

The invention relates to the technical field of video compression, in particular to a video monitoring data compression and transmission method and system, and the method comprises the following steps: analyzing the texture features of a video frame, calculating the gray gradient of each pixel, obtaining the texture change rate, detecting edge information, calculating the distribution density of edge pixels, and extracting a color histogram. And calculating the dispersion degree of color distribution. In the invention, a dynamic coding parameter adjustment mechanism based on content is introduced to analyze the video frame, a compression strategy is adjusted according to the content complexity, and the coding process can accurately meet the requirements of different video contents through comprehensive analysis on the texture change rate, the edge density and the color dispersion degree; according to the scheme, the coding parameters can be dynamically adjusted by monitoring the coded video quality in real time and comparing the coded video quality with the preset quality threshold value, and the storage and transmission of data are further optimized while the video quality is ensured.

The invention belongs to the technical field of deep learning, and particularly discloses an ultra-precision full-field displacement measurement method and system based on a convolutional variational auto-encoder. Comprising the following steps: acquiring a video when a to-be-detected structure is subjected to vibration deformation, and selecting a picture of a deformation position to construct a data set; training a deep learning model of the convolutional variational auto-encoder based on the data set; reconstructing the gray value of the original image by using the trained deep learning model to obtain a gray value containing infinitesimal displacement information; and carrying out displacement calculation on the reconstructed image by utilizing an optical flow method, and realizing ultra-precision displacement calculation on the to-be-measured structure according to gray value conversion. According to the method, the problem that the infinitesimal displacement smaller than the sensitivity limit is difficult to measure is solved, the problem that the to-be-measured structure is blocked and cannot be measured is solved by utilizing the characteristics of the generative deep learning model, and the basic data precision of displacement measurement is remarkably improved.

The invention discloses an image multi-modal feature extraction method, a ground feature classification and recognition method and a GIS image generation method. Comprising the following steps: firstly, extracting texture features from a target image by adopting a multi-directional statistical method fusing rotation invariant coding of a local binary pattern and a gray-level co-occurrence matrix, and extracting color features from the target image by adopting an LAB-HSV dual-color space collaborative analysis method to obtain features of different modes of the target image; and then according to the information values of the extracted color features and texture features, adjusting the weight ratio corresponding to the color features and the texture features so as to optimize the recognition precision of the classification model on complex ground features. And finally, according to a weight ratio corresponding to the color feature and the texture feature, performing weighted fusion on the color feature and the texture feature to obtain a corresponding multi-modal feature vector.

The invention relates to a sub-pixel edge extraction method based on an interpolation method, and belongs to the technical field of image processing and computer vision, and the method comprises the steps: obtaining a gray level image, and calculating the gradient of the gray level image in at least one direction based on a preset operator, so as to obtain the gradient amplitude of the gray level image; compressing the gradient magnitude to a preset threshold value, and calculating the gradient direction of each pixel in the gray level image; based on a preset interpolation model, in a preset period, repeatedly fitting the edge intensity in the gradient direction until a complete sub-pixel contour point set is obtained; and deploying the trained interpolation model as a sub-pixel edge extraction model so as to improve the image processing precision in edge detection. By means of the method, the problem that a traditional pixel-level edge detection method is insufficient in precision can be solved, and the detection precision and the measurement repeatability can be effectively improved in the field of high-precision measurement.

The invention provides an image quality enhancement method and system based on a cloud game, and the method comprises the following steps: firstly, a cloud end renders a game picture and generates an initial video stream; then, performing preliminary optimization on the video stream, and reducing jumping and frame loss through motion compensation of an inter-frame motion vector; the optimized video stream is transmitted to a user side, after the user side receives the video stream, image quality enhancement processing is carried out, specifically, denoising processing is carried out, and random noise and compression noise are eliminated by combining an inter-frame difference method and spatial domain bilateral filtering; sharpening enhancement: enhancing image edges and texture details by using a Laplacian operator; artifact removal: removing compression artifacts through frequency domain filtering; the dynamic contrast is enhanced, and gray level distribution is adjusted through histogram equalization; finally, the video stream after enhancement processing is presented to a user in real time, the image quality of the cloud game can be remarkably improved by gradually updating the processing result of each pixel point, the video stream is presented in real time in a high-quality mode, and the user experience is optimized.

The invention discloses a shield segment dislocation detection method and system fusing visual and geometric features, and the method comprises the steps: employing a mobile track three-dimensional laser scanning system, and rapidly obtaining the three-dimensional point cloud data of a tunnel segment; projecting the three-dimensional point cloud data of the tunnel segment to a two-dimensional image according to the scanning parameters and the image preset resolution, and mapping the laser point reflection intensity into a pixel gray value; shield segment inter-ring joints and shield segment in-ring joints are extracted respectively, and segment joint positioning is completed; and according to the seam positioning information, extracting local point clouds at the two sides of the seam, respectively carrying out circular model fitting, calculating the height difference of circular models at the two sides at the seam, and obtaining an in-ring slab staggering value. According to the method, the visual information of the point cloud reflection intensity and the geometric information of the spatial position are fused, rapid and accurate shield segment in-ring slab staggering detection is realized, and the efficiency and the accuracy are high.

To implement image synthesis without loss of surface information of SE images and shadow information of BSE images, the present disclosure proposes image processing techniques include applying data of a first quality image (low quality image) to a trained model, estimating a structural feature and a material feature of a second quality image (high quality image) corresponding to the first quality image, calculating at least one shadow datum based on the structural feature and a synthesis parameter and calculating at least one gradation datum based on the material feature and a synthesis parameter, generating a synthesized image from the at least one shadow datum and the at least one gradation datum, and outputting the synthesized image as a prediction result of the second quality image (see FIG. 8).

The invention discloses an improved filtering and local direction adaptive rust recognition method for a hull surface rust image, which comprises the following steps of: 1, performing gray processing on the hull surface rust image to obtain a gray image; 2, performing global denoising on the image after gray scale by adopting a wavelet transform and total variation fusion denoising algorithm to obtain a globally denoised image; 3, performing local denoising on the globally denoised image by adopting a self-adaptive Gaussian kernel denoising algorithm to obtain a final denoised image; 4, carrying out the edge extraction of the final denoised image through a direction self-adaptive edge detection algorithm, and obtaining a rust image; according to the method, the improved wavelet transform and total variation fusion denoising algorithm is combined, efficient noise removal is achieved, the size and the standard deviation of the filtering kernel are adjusted in a self-adaptive mode, noise of a local area is effectively processed, detail loss is avoided, and the image quality is improved.

The invention discloses a steel structure welding seam quality detection method, which comprises the following steps: acquiring a color image of a steel structure welding seam area, graying the acquired color image, performing weighted average according to weights of three color components of red, green and blue to obtain a gray value of each pixel point, selecting a neighborhood, and determining the quality of a welding seam in the neighborhood; different weights are given to pixel values in each neighborhood according to the distance between the pixel values and a center pixel, a weighted average value is calculated as a local threshold value, pixel points in the image are received, the local threshold value is compared with the pixel values of the points, the positions of weld defects are positioned, image coordinates are introduced, positioning is carried out, and the weld defects are obtained. According to the method, real-time detection of the weld defects is achieved, instant feedback is provided for quality control, the local threshold value method calculates the threshold value according to the local characteristic of each pixel point, the accuracy of defect recognition is enhanced, and the method is suitable for various weld types and illumination conditions.

The invention relates to the technical field of image processing, in particular to a capsule production quality detection method and system based on machine vision, and the method comprises the steps: collecting a grayscale image of any capsule, and obtaining edge pixel points in the grayscale image according to the gradient value of each pixel point in the grayscale image; for any edge pixel point, obtaining neighborhood pixel points of the edge pixel point, obtaining a crack edge degree of the edge pixel point according to the gray value difference and the gradient value difference between the neighborhood pixel points, and obtaining a crack edge pixel point according to the crack edge degree of each edge pixel point; according to the method, the suspected crack area is obtained according to each crack edge pixel point, the confusion degree of each suspected crack area is obtained according to the gray value and the gradient direction of each pixel point in each suspected crack area, the quality of the capsule is detected according to the confusion degree of all the suspected crack areas, and the accuracy of a capsule production quality detection result is improved.

The invention discloses a metal surface defect detection and classification method, system, equipment and medium, and the method comprises the steps: processing images of different defect types through gray normalization, adaptive histogram equalization, a Gabor filter, a maximum entropy algorithm, morphological processing and an improved local binary pattern; carrying out defect position and size calculation on the processed image, and carrying out GUI (Graphical User Interface) measurement page design; the system, the equipment and the medium are used for realizing the metal surface defect detection and classification method. According to the method, the image processing effect is remarkable, the image contrast is improved, the defect boundary identification is enhanced, the accurate segmentation of the image and the detailed optimization of the structure are realized, the image features are accurately extracted, and efficient classification training is supported; the method has the advantages of improving image quality, enhancing defect detection precision and optimizing feature extraction and analysis, and can be widely applied to the field of high-precision image analysis such as metal defect detection, industrial quality control and computer vision.

The invention discloses a space target point cloud three-dimensional reconstruction and rendering method, belongs to the field of space target scene reconstruction and rendering, solves the problem of low accuracy of space target scene reconstruction and rendering, and comprises the following steps: generating an initial sparse point cloud and a camera pose of a space target by using an SFM; performing denoising and outlier processing on the initial sparse point cloud by using a bilateral filtering algorithm to obtain an initial point cloud; distributing an initial gray Gaussian volume for each point in the initial point cloud, and projecting the initial gray Gaussian volume to a two-dimensional plane by using a projection formula in combination with a camera pose; rendering the two-dimensional projection image by using a micro-rasterization rendering technology; fusing the luminosity loss function, the structural similarity loss function and the depth loss function to construct a total loss function, and optimizing an initial gray Gaussian body attribute in the rendered image by using a stochastic gradient descent method; a self-adaptive density control mechanism is introduced to adjust the density of the initial point cloud, and a space target point cloud three-dimensional reconstruction structure is generated; according to the invention, the reconstruction and rendering accuracy of the space target scene is improved.