4481 results about "Imaging analysis" patented technology

The invention discloses a multi-modal medical image data intelligent processing system, relates to the field of medical image analysis, and is applied to multi-modal medical image whole-process analysis of CT, MRI, PET, ultrasound and the like. According to the system, different modal image features are extracted and fused through a cross-modal manifold fusion network; a semantic guidance dynamic registration engine optimizes registration parameters to ensure that the registration error is less than or equal to 1.5 mm; the multi-task collaborative diagnosis network realizes multiple tasks such as disease classification; the clinical knowledge embedding and interpretable module generates a structured report and is in butt joint with an HIS system. Meanwhile, the model is optimized through a federated learning architecture, the adaptability of newly added data is improved by more than or equal to 20%, and intelligent processing and analysis of multi-modal medical images are realized.

The invention relates to the field of image analysis, in particular to a temporal bone disease classification method and system based on a multi-modal medical image fusion technology. The method comprises the following steps: acquiring a multi-modal image of a patient, performing adaptive distortion correction, and generating a standardized image set; performing layer-by-layer anatomical structure semantic segmentation and multi-modal image fusion on the standardized image set to construct an image fusion framework; according to the image fusion framework, performing intelligent recognition on the fine structure of the temporal bone, and constructing a personalized temporal bone anatomical structure chart; performing tissue function state analysis and digital pathology dynamic simulation based on the personalized temporal bone anatomical structure chart, and constructing a digital pathology model; and performing intelligent pathological feature classification based on the digital pathological model to obtain an intelligent classification report. According to the method, rapid, efficient and accurate temporal bone disease classification is realized.

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention discloses an egg surface microcrack detection system and method based on image analysis, and relates to the field of surface defect nondestructive detection.The method comprises the steps that a process identification interface module obtains a processing process identifier of an egg in real time; the multi-angle annular light source module is provided with a multi-band annular light source array comprising visible light and near-infrared LED lamp beads which are independently controlled, a polaroid group and a beam splitter prism are integrated, and visible light polarization images and near-infrared polarization images on the surfaces of the eggs are synchronously collected; the thermal excitation enhancement unit obtains a thermal infrared image; the process filtering module extracts the axial elongation of the mechanical stress microcrack after graded transportation, and calculates the mesh fractal dimension of the thermal stress microcrack after UV disinfection; the multi-modal feature fusion module generates a dual-channel crack probability graph; and the dynamic feedback control module outputs a micro-crack risk grade and adjusts the rotating speed of the objective table and the light source intensity. The method has the advantages that accurate judgment of mechanical and thermal stress cracks is realized, and curved surface reflection interference is broken through.

The invention relates to the field of artificial intelligence, in particular to a medical image computer-aided analysis method based on deep learning, and aims to solve the problems that an existing medical image analysis method is low in high-resolution image processing efficiency, insufficient in tiny focus recognition precision, weak in model generalization ability and insufficient in multi-modal image fusion. According to the method, a lightweight multi-scale feature extraction network is constructed to improve the high-resolution image processing efficiency, a fine-grained lesion recognition module is introduced to improve the detection precision of a tiny lesion, and a self-adaptive regularization strategy is adopted to enhance the model generalization ability. And a multi-modal deep fusion mechanism is designed to make full use of complementary information of different modal images. According to the invention, medical image analysis which is more efficient, more accurate, higher in generalization ability and capable of effectively fusing multi-modal information can be realized, so that clinical application of deep learning in the field of medical images is promoted.

The invention discloses an intelligent detection method and device for fusing a medical image learning image, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring and preprocessing a bimodal medical image, and extracting a feature map through multi-scale decomposition; constructing a cross-modal correlation model, and setting a modal attention mechanism (embedding anatomical structure prior guidance feature complementation) and a morphological attention mechanism (setting lesion morphological constraint weight); the method comprises the following steps: collecting multiple types of image samples, pairing according to a focus form and an imaging mode to construct a bimodal joint data set, and correlating and labeling to generate a training data set with modal attributes; after a multi-stage iteration training model, inputting the preprocessed image to carry out feature fusion so as to obtain a fused image; and generating a lesion probability graph according to the fused image, positioning a lesion area through multi-threshold segmentation, and outputting a detection result. The system comprises a data acquisition module, a preprocessing module and the like. The method improves the accuracy and reliability of medical image detection, and is suitable for clinical multi-modal image analysis.

An agricultural image analysis system comprising at least one vision sensor configured to view a seed trench; a storage module in communication with the at least one vision sensor; a processor in communication with the storage module, the processor executing at least one machine learning module for analysis of images from the at least one vision sensor. The system including at least one laser configured to emit a beam at an open seed trench and at least one vision sensor configured to view the open seed trench and the beam. The system including a thermal camera mounted to a row unit.

The invention discloses a converter station intelligent gateway image recognition system based on a YOLOv3 target detection algorithm, and the system employs a three-stage cooperative processing architecture design, and builds seamless connection of a multispectral image collection layer, an edge calculation gateway layer, and a cloud operation and maintenance management platform layer. The invention further provides an equipment defect detection method based on the system, bimodal image data are collected through the visible light camera and the thermal infrared imager, preprocessing operation is carried out, equipment positioning and defect classification are synchronously executed by utilizing the improved YOLOv3 network, a structured detection result is output, temperature field analysis is carried out on an infrared thermal image, and the equipment defect detection result is obtained. And an abnormal heating area is identified, when defects are detected, multi-level risk response early warning is generated, and a defect diagnosis report is pushed to the cloud operation and maintenance management platform layer. Real-time image analysis of converter station equipment can be realized, the method is suitable for automatic detection of typical fault defects of the equipment, and the operation and maintenance efficiency of a power grid is improved.

The invention provides a road marking quality intelligent evaluation system based on image analysis, and relates to the technical field of road facility monitoring, and the system comprises a detection unit, a marking quality evaluation unit, a marking wear prediction unit, a GPS positioning unit, a vehicle driving information unit, a control unit and a remote central control unit. The marking quality evaluation unit is constructed based on a differential geometry theory and comprises a curvature flow edge detection module, a marking geometric characteristic manifold representation module and a multi-scale differential invariant evaluation module, the system regards a marking as a two-dimensional manifold, and the marking quality is evaluated by calculating differential geometric quantities such as a Gaussian curvature, an average curvature and a shape index. And constructing geodesic distance measurement on the feature manifold, and evaluating the completeness, visibility and reflective performance of the marked line. And the marking wear prediction unit predicts the service life of the marking based on the traffic flow information and the environment characteristic mapping relation model, and generates a maintenance suggestion.

The invention, which belongs to the technical field of image analysis and geological disaster early warning, discloses a high-precision topographic change monitoring and geological disaster early warning image analysis system comprising a deformation spatio-temporal feature sensing module, a geomechanics constraint optimization module, a multi-scale disaster evolution prediction module and a self-adaptive early warning decision module. Through deep coupling and closed-loop feedback between modules, dynamic matching of deformation confidence and mechanical constraint weight, physical enhancement of stress distribution and disaster prediction, and dual-path parameter optimization driven by early warning performance are realized, and the system adopts an InSAR technology and deep learning fusion to extract millimeter-level deformation. The physical embedded neural network is utilized to realize anomaly recognition under mechanical constraints, multi-scale disaster evolution is predicted through space-time convolution Transform, and the early warning accuracy rate can reach 95% or above after closed-loop iterative optimization.

The invention relates to the technical field of image analysis, in particular to a camellia oleifera disease and insect pest recognition system based on an image recognition technology, which realizes the structured expression of an image sequence by combining the correlation characteristics of an image time sequence and a scab evolution trajectory, forms a time axis index by using shooting time and cleans redundant images. The method effectively improves the continuity and reliability of input data, comprehensively judges the morphological dynamic state of disease spots and accurately depicts the evolutionary states of disease spot expansion, color change and the like through the comparison mode of edge texture change and main color center offset paths, remarkably enhances the staged judgment capability of disease and pest development, improves the image semantic understanding capability, and improves the image quality. Through a multi-dimension matching strategy of a closed region, a color shift direction, area jump and the like, a calibration coding sequence with traceability and staged grading capability is constructed, and the discrimination sensitivity of the system to the early stage, the middle stage and the diffusion stage of diseases is greatly improved.

The invention relates to the technical field of cloth dyeing uniformity detection, in particular to a cloth dyeing uniformity detection method and system based on image analysis, and the method comprises the steps: building a three-dimensional optical response model according to the optical characteristics of a fluff cloth fiber material; calculating to obtain a villus shadow intensity distribution diagram according to the linear polarization degree image data, and calculating to obtain a theoretical artifact reflectivity distribution diagram through a three-dimensional optical response model according to the villus shadow intensity distribution diagram and the surface normal vector mapping data; extracting an actual measurement reflectivity distribution diagram from the hyperspectral image data, and performing differential operation on the actual measurement reflectivity distribution diagram and the theoretical artifact reflectivity distribution diagram to obtain a real dyeing component diagram; dividing villus unit grids on the real dyeing component graph, and calculating a spectral fingerprint variance value of pixels in each villus unit grid; and when the spectral fingerprint variance value exceeds a preset process tolerance threshold value, judging that the corresponding fluff unit grid has a real dyeing defect. And the identification precision of the dyeing defects of the fluff cloth is effectively improved.

The invention relates to the technical field of medical image processing, in particular to a CT image intelligent analysis system for pneumonia auxiliary screening. The method comprises the following steps: firstly, preprocessing a chest CT image and detecting a candidate focus area; secondly, extracting a topological feature, a deep convolution feature and a texture statistical feature based on a persistent coherence theory from each candidate focus, and performing feature fusion through a multi-head self-attention mechanism to generate a unified focus representation vector; mapping the lesion characterization vectors to a pre-constructed radiology knowledge graph, adopting a graph neural network for reasoning, and outputting the pneumonia suspected probability and lesion classification of each lesion; and finally, performing fusion and uncertainty quantification on the analysis results of the plurality of focuses by adopting an evidence theory, and generating a comprehensive screening report. According to the method, complex-form lesions are effectively identified through topological features, accurate identification of lesion types is realized through knowledge graph reasoning, and diagnosis uncertainty quantification is provided through an evidence theory.

The invention relates to the technical field of grain detection, and discloses a grain broken rice rate online detection method and system based on image analysis. A grain broken rice rate online detection method based on image analysis comprises the steps of intelligent illumination control and high-speed synchronous imaging, self-adaptive image enhancement based on reinforcement learning, multi-stage parallel particle detection and segmentation, multi-modal feature deep fusion, robust classification based on ensemble learning, intelligent quality control and process optimization. And continuously learning and updating knowledge. A clear image is obtained through a multi-angle linear array camera and a stroboscopic light source, a reinforcement learning agent is adopted to carry out adaptive image enhancement, a deep learning network is used to carry out particle detection segmentation, multi-dimensional features are extracted, accurate identification is realized through an integrated classifier, and a digital twin model is established to carry out process parameter optimization. According to the invention, accurate online detection of the broken rice rate of grains can be realized in a high-speed flowing state, and the detection efficiency and accuracy are improved.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention relates to the field of medical image analysis, in particular to a deep learning prediction system and method based on multi-modal thyroid cancer lymph node metastasis, and the system comprises a data collection module, a preprocessing module, a nodule segmentation module, a feature extraction module, a feature fusion module, a metastasis prediction module, an interpretability analysis module and a result display module. An ultrasonic image, an elastic imaging image, an ultra-micro blood flow image and clinical index data of a patient are integrated, an improved U-Net algorithm is used for precise segmentation of a thyroid nodule region, a multi-branch deep network is used for extracting multi-modal features, a dynamic weight fusion algorithm is used for integrating the features, and the accuracy of the thyroid nodule region is improved. According to the method, the thyroid cancer lymph node metastasis state (non-metastasis, central region metastasis or lateral neck metastasis) is predicted, meanwhile, a two-dimensional interpretability framework of Grad-CAM activation diagram and SHAP value contribution degree analysis is introduced, an intuitive prediction basis is provided for doctors, and the thyroid cancer lymph node metastasis prediction accuracy is remarkably improved.

The invention discloses a multi-modal collaborative video sequence segmentation method. The method comprises the following steps: obtaining a multi-scale local feature matrix and a multi-scale global feature matrix of an image sequence; obtaining a multi-scale text feature matrix of the text sequence; obtaining a multi-scale local-global fusion feature matrix of the multi-scale local feature matrix and the multi-scale global feature matrix; obtaining a multi-modal fusion feature matrix of the multi-scale local-global fusion feature matrix and the multi-scale text feature matrix; and utilizing a decoder of the pre-trained large model to predict and generate a segmentation mask, and outputting a semantic segmentation map. The video sequence segmentation method is stable in performance when facing complex and changeable scenes, does not need to depend on a large amount of labeled data, reduces the training cost, and is suitable for various practical application fields including intelligent monitoring, automatic driving, medical image analysis and the like.

The invention discloses a heterogeneous double-flow fusion method and system for diabetic retinopathy grading. The method comprises the following steps: obtaining an output result of diabetic retinopathy grading by utilizing a heterogeneous double-flow architecture; processing an input fundus image into images with different resolutions; extracting global context features from the low-resolution image by using a lightweight visual Transform model distilled by composite knowledge, and extracting local focus features from the high-resolution image by using a convolutional neural network model; performing interactive fusion on the global context features and the local focus features of the double-branch architecture through a symmetric bidirectional cross attention fusion module to obtain enhanced fusion feature representation; and finally, inputting the fusion features into a classifier, and outputting a severity grading result of the lesion. The method aims at improving the accuracy and robustness of hierarchical diagnosis through deep analysis of global information and local details, and can be applied to the medical fields of clinical computer-aided diagnosis, eye image analysis and the like.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention relates to the technical field of image analysis, and discloses a multi-modal emotion recognition method and system based on hypergraph diffusion and evidence fusion, a terminal and a storage medium, and the method comprises the steps: carrying out the random shielding of a data set through a randomly generated mask, thereby simulating the random data missing condition, and carrying out inverse sampling on the preprocessed simulation data by using a trained conditional diffusion model to obtain a training set in which missing modals are complemented so as to train an emotion classification network, and finally carrying out emotion recognition. According to the method, through dual-channel evidence fusion, uncertainty is estimated at a feature source level and a discrimination level at the same time, so that adaptive evidence fusion is realized, the condition of performance reduction caused by modal loss is reduced, potential features of the lost modal are explicitly recovered in a feature space, and the accuracy of final emotion recognition is improved.

The invention relates to the technical field of image segmentation, in particular to an automatic segmentation method and system for an echocardiogram of the department of cardiology, and the method comprises the following steps: based on image data of the echocardiogram, extracting gray level distribution, edge feature and texture feature information, analyzing gray level change amplitude, screening gray level change abnormal regions, and recognizing connectivity features. According to the method, the segmentation accuracy is effectively improved by extracting image gray, edge and texture features and identifying abnormal regions, noise and artifact interference are reduced by optimizing low connectivity regions, and the segmentation accuracy is improved. A problem area is analyzed and positioned in combination with multi-frame gray level change, a segmentation result is adjusted, the processing stability and consistency are enhanced, meanwhile, an optimized alarm node is output based on a frequency trend, more accurate and stable heart image analysis is supported, and the clinical application practicability is improved.

The invention relates to a solidified soil proportion and strength prediction method and system based on SEM image analysis, and belongs to the technical field of solidified soil strength testing. Comprising the following steps: S1, measuring physical indexes of disturbed soil; s2, multi-working-condition sample preparation and maintenance; s3, fractal dimension calculation of the SEM image is carried out; s4, unconfined compressive strength testing; s5, establishing a microstructure inversion model and a macroscopic strength prediction model; and S6, proportion optimization and strength prediction: utilizing the microstructure inversion model and the macroscopic strength prediction model to carry out solidified soil proportion optimization or strength prediction. According to the method, the quantitative relation between the microstructure (fractal dimension) and the macroscopic strength is established through SEM image analysis, the defects in the prior art are overcome, and a brand new technical path is provided for resource utilization of disturbed soil.

The invention relates to the technical field of image analysis, in particular to a cushion foaming forming quality detection method and system based on machine vision, and the method comprises the steps: collecting image data, carrying out the iterative screening of the image data, and selecting a detection sample; the method comprises the following steps: collecting a three-dimensional point cloud of a detection sample, calculating the flatness of a seat cushion by using the three-dimensional point cloud, carrying out modeling by using polarized light reflection to obtain surface cell uniformity, and carrying out feature extraction on the three-dimensional point cloud through a three-dimensional mapping model to generate a three-dimensional semantic model; extracting mechanical characteristics by using the time sequence pressure map, and generating a cushion digital model; variational self-coding is carried out on the cushion digital model, and an abnormal feature map is generated; performing information extraction and pixel-by-pixel multiplication on the abnormal feature map by using a learnable convolutional layer to generate an abnormal enhanced map; and inputting the abnormal enhancement graph and the seat cushion digital model into a multi-channel network model for final evaluation. According to the method, the image data is analyzed, and the performance of the cushion is subjected to index quantification, so that the deep detection of the quality of the cushion is realized.

The invention relates to the technical field of oral implantation monitoring, and discloses a monitoring method for analyzing oral implantation health based on using images. The method comprises the following steps: acquiring an optical image data sequence of an oral implanting region by adopting optical scanning equipment, constructing a three-dimensional oral tissue model containing gingival contour features and an implant position, and identifying actual position distribution information of an implant in gingival tissue; and extracting an implant health index set comprising an implant marginal definition value, a surrounding gingival red and swollen area proportion and a gingival atrophy degree value. When any index exceeds a preset health threshold interval, abnormal early warning information is generated, scanning parameters are dynamically adjusted, and images are collected again for iterative monitoring. According to the method, dynamic and accurate monitoring of oral implant health can be realized, health abnormity of the implant can be found in time, and support is provided for long-term health maintenance of the implant.

The invention relates to the technical field of geological reservoirs, in particular to a reservoir microcosmic connectivity characterization method based on pore throat boundary intelligent identification, which comprises the following steps of: performing threshold segmentation processing on a rock casting body slice by using image analysis software, and depicting partition images of pore throats and skeleton particles; based on the partition image, combining image analysis software to measure and extract the diameter, perimeter and area of the pore throat, and calculating quantitative parameters of the pore throat structure; constructing a one-dimensional Gaussian distribution model of the nuclear magnetic logging of the reservoir; based on a one-dimensional Gaussian distribution model, establishing a Gaussian collaborative factor for pore structure evaluation; based on the quantitative parameters of the pore throat structure and the established Gaussian collaborative factors, establishing a multi-factor evaluation mathematical method to establish a multi-element evaluation model of the pore structure; and performing multivariate nonlinear fitting on the porous structure multivariate evaluation model, the nuclear magnetic movable fluid saturation and the reservoir pressure measurement fluidity, and constructing a quantitative characterization model for characterizing the reservoir microscopic connectivity. According to the method, the microscopic connectivity of the reservoir can be accurately, rapidly and quantitatively evaluated.

The invention relates to the technical field of medical image analysis, computer vision and deep learning, in particular to a texture perception and multi-scale feature adaptive fusion hip joint osteophyte detection method. According to the method, firstly, a bone texture feature extraction module is used for carrying out feature extraction and enhancement on a hip joint X-ray image, a parallel double-branch structure is adopted, gradient and space structure features are extracted through a Sobel operator and pooling operation, and feature maps are fused; then, the fused features are input into a double-backbone network, the first backbone network extracts local fine textures and long-range dependence features by combining convolution, self-attention and a gating mechanism, and the second backbone network extracts multi-scale semantic features through hierarchical grouping and depth separable convolution; and double-trunk output is dynamically weighted and fused through an adaptive fusion module, features are optimized through a multi-scale semantic fusion module, and finally the position and confidence of osteophyte are output.

An online system receives from a device associated with a picker, an image of an order delivered at a location associated with the order for a user and accesses a plurality of features about the order to output a likelihood that the delivered order in the received image is erroneous. The online system applies a machine learning model to the received image of the order and the plurality of features of the order. The machine learning model is trained to predict a likelihood that the delivered order is erroneous. The online system determines that the delivered order is erroneous and transmits a warning message to the device associated with the picker about the identified potential delivery error.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention discloses a marine ranching raising site selection method and system, applied to the field of data processing, and the method comprises the steps: obtaining seabed environment parameters of each target sea area; based on the time sequence change information of the submarine environment parameters, at least obtaining a temperature field distribution result and a biological field distribution result; determining full life cycle information of a to-be-bred object, and determining a corresponding growth demand parameter set; performing space-time matching analysis on the temperature field distribution result, the biological field distribution result and the growth demand parameter set, and calculating a comprehensive raising index corresponding to each target sea area; and obtaining a site selection decision result of each target sea area at least based on the shipping channel information, the ecological protection information and the corresponding comprehensive raising index of each target sea area. According to the marine ranching raising site selection method provided by the invention, scientific and effective marine ranching raising site selection can be realized by comprehensively considering seabed time sequence sensing data, biological image analysis and full life cycle requirements of cultured objects and combining a multi-objective optimization algorithm.

The invention belongs to the technical field of image processing, and particularly relates to a cable shielding layer weaving density detection system based on image analysis, which comprises an image correction module, a frequency domain analysis module, a self-adaptive filtering module and a density defect detection module, obtaining the maximum peak point of the centralized complex frequency spectrum; constructing a band elimination filter according to the maximum peak point, filtering the centralized complex frequency spectrum through the band elimination filter to obtain a filtered complex frequency spectrum, executing inverse Fourier transform to obtain a filtered space image, and binarizing the filtered space image through a threshold segmentation algorithm to obtain a pore mask; the porosity is obtained according to the ratio of the non-pore pixel points to the total number of the pixel points; and judging the weaving defect of the shielding layer according to the porosity. According to the method, the strong periodic signals of the weaving textures are automatically identified and filtered in the frequency domain, so that non-periodic defects such as pores are effectively highlighted, the technical problem that periodic textures cover defect signals is solved, and the accuracy of defect detection is improved.