1169 results about "Surface map" patented technology

The invention discloses a polyethylene plastic hollow plate defect detection method and system, and relates to the technical field of nondestructive testing, the system is composed of a plurality of functional modules, and the system comprises: a multi-mode sensing module for collecting multi-source data including a hollow plate surface image, three-dimensional morphology data and an ultrasonic reflection signal; the historical database construction module is used for performing space-time alignment and feature level fusion on the multi-source data to generate a composite defect feature matrix; collecting ultrasonic signal waveforms of known defects, extracting characteristic parameters, and establishing a historical waveform library; calculating a characteristic parameter classification range of each type of defects based on a historical waveform library; judging whether the detection waveform parameter falls into a classification range corresponding to the processing noise or not, and if so, rejecting the signal; otherwise, keeping; and the defect area identification module is used for dividing the surface of the hollow plate into uniform grids based on the surface image of the hollow plate, and counting the density of the residual ultrasonic detection sources in each grid.

The invention provides an LED product surface defect detection method, which comprises the following steps: carrying out defect feature extraction on a preprocessed LED surface image by adopting a multi-scale morphological algorithm, and respectively carrying out morphological opening operation and closing operation on surface recesses and fine scratches by setting structural elements with different sizes and shapes to obtain candidate regions of the recesses and the scratches; according to the geometric dimensions, morphological characteristics and spatial distribution detail attributes of the recesses and scratches, constructing defect feature vectors, and performing classification and severity evaluation on defect candidate areas through a support vector machine algorithm to obtain the types, positions and quantitative description parameters of defects; and updating the light source parameter database and the defect feature database according to a quality detection result of the LED product, analyzing an association rule between defects and light source parameters through a data mining algorithm, and optimizing a light source parameter configuration strategy and a defect detection model.

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention discloses an aluminum alloy surface defect detection method and system using deep learning, and relates to the related field of detection performed in combination with deep learning, and the method comprises the following steps: carrying out image acquisition on an aluminum alloy finished product, constructing a surface image data set to extract multi-scale surface features, and constructing a feature enhanced image set to carry out defect detection, and a defect detection result is obtained to dynamically optimize aluminum alloy production process parameters, and a process adjustment instruction is generated and fed back to a production system. The technical problems that traditional detection mostly depends on manual visual inspection or a machine vision system based on rules, imaging distortion, small defect missing detection and process adjustment are caused are solved, and the technical effects that deep learning is conducted through the double-branch network, the defect detection efficiency and the production yield are improved, and control over the aluminum alloy manufacturing quality is met are achieved.

The embodiment of the invention discloses a circuit board defect automatic detection method and system based on machine vision, which are used for improving the precision and efficiency of circuit board defect detection and repair, and the method comprises the following steps: obtaining a surface image data set of a target circuit board; performing image preprocessing operation on the surface image data set to generate a preprocessed image data set; inputting the preprocessed image data set into a pre-trained defect feature extraction model, and performing multi-level feature fusion processing on each piece of image data through the defect feature extraction model to generate a multi-dimensional defect feature set; determining a defect positioning information set of the target circuit board according to a matching degree calculation result between the multi-dimensional defect feature set and a preset defect feature template; and transmitting the defect positioning information set to a defect repair control terminal, and triggering the defect repair control terminal to generate a repair path planning instruction according to the defect positioning information set.

The invention relates to the technical field of industrial automatic quality inspection, and provides a PCBA surface defect detection method and system based on deep learning and a medium, and the method is used for carrying out defect detection on a preset PCBA board. Comprising the following steps: acquiring a surface image of a PCBA board according to a preset multi-angle light source and a high-resolution camera, and performing adaptive illumination compensation and noise removal processing on the surface image to generate a standardized image; performing multi-scale segmentation on the standardized image to obtain image blocks including local details and a global structure; constructing a double-branch deep learning model, wherein the double-branch deep learning model comprises a backbone network, a multi-scale feature fusion module and a defect detection branch; inputting the image blocks into a double-branch deep learning model, and outputting a thermodynamic diagram and probability distribution by the double-branch deep learning model; performing binarization processing on the thermodynamic diagram by using a dynamic threshold segmentation algorithm to generate a defect mask; and outputting a defect detection result of the PCBA board according to the defect mask and the probability distribution, and completing the defect detection of the PCBA board.

The invention provides an engineering construction defect automatic detection and classification method based on deep learning, and the method comprises the steps: obtaining a welding seam surface image through the shooting of an unmanned plane, and carrying out the denoising and illumination normalization processing of the welding seam surface image, and obtaining a standardized image; welding seam surface texture features are extracted from the standardized image, a convolutional neural network is adopted to analyze the spatial distribution characteristics of textures, and vectorization processing is carried out to obtain texture feature vectors; segmenting a weld surface corresponding to abnormal region distribution by adopting a region growing algorithm, and analyzing pore and weld discontinuity in combination with the texture feature vector to obtain a defect candidate region; performing threshold division on the sizes and the numbers of the defects according to the defect types and the feature vectors of the candidate regions to obtain a severity grading result of each type of defects; and severity features are extracted from a grading result, and a Bayesian network is adopted to fuse texture feature vectors and defect type labels to obtain a welding quality evaluation score.

The invention relates to the technical field of grinding, in particular to a self-adaptive force control grinding system and method for a complex curved surface machining robot. Three-dimensional point cloud data of a workpiece are obtained through machine vision, gridding processing is carried out, and normal vector and curvature characteristic parameters are calculated; performing operation area division and operation path planning based on curvature characteristics; calculating operation parameters according to the normal vector and curvature characteristics; a self-adaptive impedance control algorithm is adopted, the polishing force is monitored in real time through a force sensor, and the rigidity and damping parameters of the robot are dynamically adjusted; and performing defect identification on the surface image of the processed workpiece by using the deep learning model, and when defects are identified, re-planning the operation path. According to the method, self-adaptive grinding of the complex curved surface workpiece is achieved, and the grinding efficiency and quality of the complex curved surface can be improved.

The invention relates to the technical field of intelligent sorting, and discloses a multi-mode defect feature fusion intelligent sorting method and system, and the method comprises the steps: obtaining and preprocessing a surface image, infrared thermal imaging and voiceprint vibration data of an object; analyzing and generating multi-modal defect feature parameters, fusing to form a fused feature vector set, and constructing a defect detection reference set; performing dynamic matching verification on the multi-modal data based on the reference set, and analyzing the mismatching state of the surface texture and the thermal distribution by using a space alignment technology; and generating a defect form deviation degree index according to a verification result, and judging whether a sorting action is triggered or not. The system comprises a multi-modal acquisition module, a feature fusion modeling module, a form association verification module and a sorting judgment module which are used for respectively realizing data acquisition preprocessing, feature fusion modeling, cross-modal association analysis and sorting decision. Through multi-modal data fusion and cross-modal quantitative analysis, the comprehensiveness of defect detection and the sorting accuracy are improved.

The invention relates to an intelligent vacuum coating quality monitoring method based on artificial intelligence, which comprises the following steps: collecting process parameters in real time through a coating equipment sensor, dynamically monitoring vacuum degree change, deposition rate deviation and temperature gradient, and standardizing data flow to obtain a quantitative trend curve of process parameter fluctuation; combining the defect microcosmic feature description set with gas flow fluctuation and power supply voltage jitter to obtain a correlation analysis result of process parameters and defect forms, and calibrating defect types according to a defect feature library to obtain an updated defect classification basis; and inputting real-time new surface image data through the updated defect classification basis, performing defect detection for illumination reflection differences and stripe deflection angles, obtaining a preliminary defect classification result, and adjusting classification weight parameters.

The invention discloses a welding detection method and application thereof in welding monitoring of an LNG (liquefied natural gas) storage tank. According to the scheme, 3D point cloud data, surface image data and cross-sectional image data acquired from a welding seam by combining a 3D laser scanner, an industrial camera and an ultrasonic phased array probe are subjected to multi-modal data fusion; the fusion data is input into a trained defect recognition model, so that the weld joint can have a more perfect data source in quality detection classification, and a reliable data basis is provided for weld joint detection; besides, according to the scheme, a welding seam model based on digital twinning is constructed on the basis of fusion data, in this way, when the welding seam has defects caused by abnormal welding, the welding seam model constructed by the digital twinning model can be used for positioning the defects and outputting local welding seam model data with the defects, and meanwhile, by combining a detection result, the defect detection accuracy is improved. And more visual and reliable detection feedback can be provided for background management personnel.

The invention relates to the technical field of image processing, and discloses a vehicle surface defect identification method and system based on artificial intelligence. The method comprises the following steps: acquiring a vehicle surface image through polarization filtering multi-angle imaging; processing the standardized image by applying HSV color conversion and texture equalization; constructing three-dimensional deformation features in combination with parallax analysis, and extracting defect feature vectors; and identifying defects by using the position-aware convolutional neural network, and outputting a severity thermodynamic diagram. According to the invention, in a complex illumination environment, tiny defects of vehicle surfaces with various colors and materials are accurately identified and evaluated, and accurate three-dimensional positioning and severity quantification of the defects are realized at the same time.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention discloses a 3D printing process intelligent control method and system based on real-time feedback, and relates to the technical field of 3D printing, a data acquisition module collects a printing layer surface image and printing data in real time, constructs an image data set and a printing data set, and performs cleaning and standardization processing on the data in a preprocessing module; the image analysis module analyzes surface defects of a printing layer by using a convolutional neural network, generates a defect score D, compares the defect score D with a preset defect score threshold D1, evaluates the state of the printing layer and triggers early warning, and the comprehensive evaluation module calculates a crack generation probability Z by combining printing data and the defect score; and the intelligent control module compares the crack generation probability Z with a preset crack generation probability threshold value Zl, evaluates the printing state and feeds back and controls printing parameters according to a result, so that crack and structure defects in the printing process are effectively prevented and reduced, and the printing precision and the finished product quality are improved.

The invention discloses a surface defect small target detection method based on multi-scale feature interaction. Comprising the steps that a surface image, collected in real time, of a to-be-detected product is input into a detection model, and after the detection model processes the surface image, an image marked with possible defect classification categories, bounding box coordinates and defect target existence confidence is output; wherein the detection model is a model taking a YOLOv11 network as a basic structure and comprises a backbone network, a neck network and a detection head which are sequentially connected in series; a multi-scale feature extraction SMK module and an attention mechanism SELA-S module are introduced into the backbone network; two feature fusion Fault modules are introduced into the neck network. According to the method, the multi-scale target features under the complex background can be effectively extracted and fused, and the method has good processing performance for distinguishing defects in the picture from the complex background and multi-scale target detection. And in a surface defect detection task, the method can be accurately focused on a defect area.

The invention relates to the technical field of industrial product surface quality detection, in particular to a flexible display module surface defect image recognition method, which comprises the following steps: acquiring a plurality of surface images of a flexible display module under different light sources and carrying out distortion removal processing on the surface images; reconstructing and generating three-dimensional reference point cloud data representing the current curved surface form of the module; re-projecting the distorted image to the ideal rigid plane according to the relationship, generating a plurality of corrected images, and generating a plurality of corrected images to eliminate geometric and luminosity distortion introduced by flexible deformation; obtaining a defect candidate area binary image; and extracting a multi-dimensional feature vector of the defect candidate region from the binary image of the defect candidate region, and classifying the feature vector by using a pre-trained defect classification model to obtain a defect identification result. Through the three-dimensional reference point cloud reconstruction and image re-projection technology, the problem of geometric distortion caused by surface deformation of the flexible display module is effectively solved, and misjudgment and missed judgment are avoided.

The invention relates to the technical field of quality detection, in particular to a product quality control method and system based on machine vision, and the method comprises the following steps: obtaining product surface image data, calculating the gray gradient value of each pixel, extracting the gray gradient change rate, recording the gradient amplitude and direction information, and generating product surface gradient data. According to the method, through pixel-level gray scale gradient calculation, the product local feature expression ability is improved, multi-scale gradient change trend analysis is combined, the accurate recognition ability of a product defect area is improved, through texture direction angle calculation and vector field construction, the direction change anomaly detection reliability is enhanced, and the direction change anomaly detection accuracy is improved based on the combination of a direction deviation accumulated value and an abrupt change threshold value. Effective identification of a structure sudden change area is ensured, adjustment is carried out for curvature continuity abnormal points, defect boundary fitting precision is optimized, defect area internal gradient distribution and boundary feature comparative analysis are carried out, accurate classification of defect types is realized, and stability and adaptability of automatic product quality detection are ensured.

The invention relates to a lightweight concrete laminated slab crack detection method, belongs to the technical field of crack detection, and solves the problems of low detection efficiency and low detection precision in narrow space, insufficient light or complex background environment in the existing crack detection technology. The method comprises the following steps: acquiring a surface image of a to-be-detected concrete laminated slab; performing crack detection on the image through the trained crack detection model to obtain a crack detection result; the crack detection model comprises a crack feature extraction module, a crack feature fusion module and a crack detection module; the crack feature extraction module is used for extracting different scale features of the image; the crack feature fusion module is used for fusing the features of different scales to obtain a fusion enhanced feature map, a multi-scale fusion feature map and a context guide fusion feature map; the crack detection module outputs a feature map based on the crack feature fusion module to obtain a crack detection frame and a crack segmentation mask; and obtaining the width and length of the crack based on the crack segmentation mask.

An improved YOLOv8-based industrial pipeline defect detection method and system are provided. The method includes: acquiring a pipeline surface image; and recognizing a defect position and a defect type in the pipeline surface image by using a pipeline defect detection model. The model is based on an improved YOLOv8 network, which replaces the original means with WIoU loss and a Sophia optimizer during training, and a final model can quickly and accurately recognize the defect position and the defect type in the pipeline surface image. Compared with a conventional YOLOv8 algorithm, training stability, convergence speed, and recognition accuracy are improved by replacing a CIoU loss function with a WIoU loss function. An official AdamW optimizer is replaced with a Sophia optimizer, training time of the model can be greatly shortened, a lot of computing resources can be saved, and less memory is occupied.

The invention discloses an image analysis method and system for concrete apparent quality defect detection, and relates to the technical field of concrete apparent quality defect detection.The method comprises the steps that a camera device is used for collecting concrete surface images in a specified distance interval, and the optical axis of a camera lens is controlled to be perpendicular to the concrete surface; carrying out image preprocessing on the collected concrete surface image, and carrying out defect type image marking; carrying out key feature extraction on the preprocessed concrete surface image by adopting a convolutional neural network to obtain key feature information; constructing a multi-algorithm comparison verification framework, performing cross validation in combination with the key feature information to obtain defect detection results and evaluation results of a plurality of defect detection models, optimizing model parameters in combination with a confusion matrix, and determining a final concrete surface detection model; obtaining an apparent quality defect detection result based on the defect detection result and a preset quality defect grading threshold value; the efficiency of concrete apparent defect detection is improved.

The invention relates to the technical field of image processing, in particular to an intelligent detection method and system for shaft forgings. The method comprises the following steps: acquiring gray values, positions and gradient directions of all pixel points in a surface image of the shaft forging; clustering all pixel points in the surface image by using a condensed hierarchical clustering algorithm to obtain a plurality of regions; calculating the suspicious degree of each region; determining a scratch burr area in the surface image of the shaft forging to realize intelligent detection of the shaft forging; in each round of iteration process of the condensed hierarchical clustering algorithm, for two to-be-combined clusters in each round of iteration process, calculating the separation degree of the two clusters; calculating the feature similarity of the two clustering clusters; determining the number of clustering clusters of the current iteration; a plurality of regions of the surface image of the shaft forging are determined. By dynamically adjusting the number of clusters, the problem of wrong merging or premature iteration stopping of a traditional condensed hierarchical clustering algorithm under the fixed number of clusters is avoided.

The invention provides a road surface defect detection method and system based on multi-sensor fusion, and the method comprises the steps: 1, capturing a road surface image through an RGB camera, and obtaining the three-dimensional point cloud data of a road surface through LiDAR; 2, performing feature extraction on the road surface image captured by the RGB camera by using a YOLOv5 network; step 3, processing the road surface three-dimensional point cloud data acquired by the LiDAR by using a Point Net + + network; step 4, carrying out deep fusion on the image features extracted by the YOLOv5 and the point cloud features extracted by the Point Net + +; and 5, detecting and classifying the road surface defects by adopting a quantitative and qualitative combined method. According to the method, an RGB camera and LiDAR (light detection and distance measurement) are adopted, a YOLOv5 network is combined with PointNet + +, high-precision and real-time detection and classification of road surface defects are achieved, and the defects of a traditional method are overcome.

The invention relates to the technical field of image processing, in particular to a high-voltage power supply element defect identification method based on computer vision. The method comprises the steps of collecting a surface image of a high-voltage power supply element; the method comprises the following steps: acquiring a grayscale image and a connected domain thereof, acquiring a main direction of the connected domain, constructing a width sequence based on a length perpendicular to the main direction, acquiring a width change coefficient according to the width sequence, and acquiring ambiguity of a pixel point based on a pixel point neighborhood grayscale value; constructing a window for the edge pixel points, obtaining the correlation of the edge pixel points according to the classification of the connected domains in the window, obtaining the similarity between the connected domains according to the Fourier descriptors of the connected domains, and obtaining the scratch integrating degree of the edge pixel points based on the similarity; and screening the edge points of the scratch area according to the scratch integrating degree of the edge pixel points and the ambiguity of the edge pixel points, and completing defect identification. According to the invention, the surface defect detection efficiency of the high-voltage power supply element is improved.

The invention provides a surface quality monitoring method and system based on image-signal multi-modal data, and relates to the technical field of machining process detection, and the method comprises the steps: obtaining a workpiece surface image and a main shaft vibration acceleration signal in a milling process, inputting the workpiece surface image and the spindle vibration acceleration signal into a surface roughness classification model to obtain a machining state recognition result; wherein in the surface roughness classification model, shallow texture features of a workpiece surface image are extracted through an image processing module, and frequency domain features of a spindle vibration acceleration signal are extracted through wavelet transform and a frequency attention mechanism; and splicing projection features of the shallow texture features and the frequency domain features into a combined feature vector, carrying out dynamic weight distribution and fusion on the combined feature vector based on a self-adaptive fusion strategy of a gated attention mechanism to obtain weighted fusion features, inputting the weighted fusion features into a grade classifier, and outputting the grade of the surface roughness.

The invention relates to the field of deep learning, and provides a strip steel surface defect detection method and system, and electronic equipment, and the method comprises the steps: obtaining the state information of a to-be-detected strip steel, the state information at least comprising a surface image of the to-be-detected strip steel; inputting the state information into a pre-constructed detection model for defect detection, wherein the detection model is constructed by fusing an improved spatial pyramid module and a polarization self-attention mechanism on the basis of a Yolov8 model; the improved spatial pyramid module comprises an SPPCSPC layer and a variable convolutional layer, and the polarization self-attention mechanism comprises a channel branch and a spatial branch; and determining the surface defect of the to-be-detected strip steel based on the detection information output by the defect detection model. The method and the device are used for overcoming the defect of poor detection precision when defect detection is carried out on the surface of the strip steel in the prior art, and compared with an existing Yolov8 model, the scheme provided by the invention has the advantage that the detection precision is obviously improved when the defect detection is carried out on the surface of the strip steel.

The invention provides an aluminum profile defect analysis method and system based on texture features, and relates to the technical field of edge detection, and the method comprises the steps: obtaining an aluminum profile surface image, and determining an extrusion direction; dividing the image into a plurality of local blocks, carrying out gradient direction and amplitude calculation on each block, and judging whether the block belongs to a high-confidence-coefficient texture region or not by combining a direction difference value and confidence coefficient; a first suppression coefficient is executed on the high-confidence-coefficient texture region for primary reduction to form a first processing image, then the updated image is judged again, a second suppression coefficient with higher strength is applied to the region still having obvious texture features, and a second processing image is generated; and finally, defect identification is carried out on the weakened image through edge detection, and residual texture false edges are removed in combination with direction consistency or connectivity analysis. The method has the advantages of light weight, low computing power consumption and high accuracy, and can be applied to online detection and quality control of the surface flaws of the aluminum profile in industrial production.

The invention relates to the technical field of cloth flaw detection and deep learning, in particular to an MAC-YOLO-based cloth flaw detection method, which comprises the following steps of (1) constructing an MAC-YOLO network, and improving the MAC-YOLO network based on a YOLOv8 network; (2) collecting a cloth surface image data set, carrying out defect type labeling on defect images, and randomly dividing the data set into a training set and a test set according to a proportion of 8: 2; (3) the training set is used for training the MAC-YOLO network, an AdamW optimizer is adopted, a learning rate strategy is adjusted in stages, and a loss function comprises binary cross entropy loss of classification branches and distribution focus loss and complete intersection-to-union ratio loss of regression branches; and (4) detecting the input cloth surface image by using the trained MAC-YOLO network, and outputting the category and position prediction map of the flaws.When the extreme length-width ratio image data is processed, various flaws can be accurately detected.

The invention provides a laser scribing track control method and device based on visual inspection. The method and device are applied to a laser scribing system. The laser scribing system comprises a visual collection module, an edge calculation module, a motion control module and a process execution module. The track control method comprises the steps that the visual collection module collects a workpiece surface image and sends the workpiece surface image to the edge calculation module; the edge calculation module processes the workpiece surface image to determine a target track of a P1 curve of the workpiece surface, processes the target track of the P1 curve to generate a laser scribing track of the P1 curve, a laser scribing track of a P2 curve and a laser scribing track of a P3 curve, and sends the laser scribing tracks of the P1 curve, the P2 curve and the P3 curve to the motion control module; and the motion control module controls the process execution module to perform real-time offset lineation based on the laser lineation tracks of the P1 curve, the P2 curve and the P3 curve, so that the lineation precision and consistency are improved.

The invention provides a numerical control machining defect real-time detection method and system based on AI image recognition and a medium, and belongs to the technical field of intelligent manufacturing and machine vision crossing. The method comprises the following steps: collecting part surface image data according to a preset frequency, and separating a part from a background through noise reduction, contrast enhancement and a dynamic threshold algorithm of a data preprocessing module; inputting the processed image into a multi-modal feature fused AI detection model, and extracting and fusing multi-modal features to judge the type and position of a defect; and processing the image by using a parallel computing architecture, feeding back a detection result to a numerical control processing control system in real time, and visually displaying the detection result on a monitoring interface. According to the method, real-time and accurate detection and feedback control of numerical control machining defects are realized, and the machining quality and efficiency are effectively improved.

The invention relates to the technical field of defect detection, in particular to a heat-conducting adhesive tape defect detection method and system based on machine vision, and the method comprises the following steps: obtaining a surface image of a heat-conducting adhesive tape, extracting a texture trend and marking an aggregation block, adjusting the trend and offset of a connecting line segment, and analyzing the angle change of a sideband node. And recognizing a skeleton trend reverse section and closing a boundary, and extracting a continuous direction changing region to obtain a defect structure image annotation layer. According to the method, the turning position in the sideband region is extracted by constructing the direction aggregation graph of the texture aggregation block, enhancing the direction identification of the direction of the regional texture, combining the grouping organization and coordinate offset processing of the line segment path, enhancing the continuous association of the image structure, and by means of the repeated distribution characteristic of the angle mutation node; and through a skeleton trend reverse paragraph and boundary closing mode, an identification range of a structure disturbance block is expanded, and coherent tracking and multi-level labeling of a structure abnormal region are completed by combining a spatial relationship between a contour trend difference and a turning point.