95 results about "Phase congruency" patented technology

The invention discloses a photoelectronic device surface defect visual detection method and system based on image recognition, and the method comprises the steps: carrying out the processing and fusion of three-waveband image data, and obtaining a three-waveband composite image; constructing a DFD-Net double-branch deep network model, inputting a three-band synthetic image into the model, capturing multi-scale defect geometric features through an ASPP module with increasing voidage, extracting reflective insensitive texture features through a phase-consistent convolutional layer, and performing feature fusion by using a gated cross attention mechanism to obtain a multi-scale defect feature fusion model; performing coarse positioning on the fused feature image based on an improved YOLOv5 target detection algorithm to obtain a defect area image, and calculating a pixel-level mask of the defect area image to obtain a segmented image; and positioning a defect boundary of the segmented image through an NMS non-maximum suppression algorithm, and outputting defect coordinates and size information. And the surface defect detection efficiency of the optoelectronic device is improved.

The invention discloses a multi-modal remote sensing image matching method, particularly relates to the technical field of remote sensing image processing, and is used for solving the problem of multi-modal image matching. The method mainly comprises the following steps of: 1, improving a phase consistency model, and constructing a phase-moment weighted joint direction feature in combination with a maximum moment and a minimum moment to replace the feature expression of the traditional image gradient; 2, implementing a point product fusion strategy on the phase-amplitude characteristics extracted by the phase consistency model and the maximum moment, and constructing phase-moment weighted joint amplitude characteristics; and 3, on the basis of the steps 1 and 2, identifying the direction of the feature points and screening local peak values to determine the main direction. Three values adjacent to a peak value are selected, and the peak value position is interpolated through parabola fitting so as to improve the matching precision; and step 4, constructing a logarithm polar coordinate descriptor based on regularization non-uniform partition to generate a feature description vector. Through the mode, high-precision and high-efficiency matching of the multi-mode remote sensing image can be realized.

The invention relates to the technical field of machine vision image analysis, in particular to a lubricating oil abrasive particle quality analysis method and system based on machine vision, and the method comprises the steps: obtaining a thickness distribution diagram of an oil residual layer on the surface of an abrasive particle through a gray ratio relation between a first transmission image and a second transmission image; performing optical compensation on the first transmission image to obtain a final compensation image, and performing phase consistency edge detection to obtain an abrasive particle edge enhanced image; calculating the polarization reflectivity ratio of each abrasive particle area; extracting the mirror reflection intensity difference of each abrasive particle in the abrasive particle edge enhanced image; and when the abrasive particles are analyzed as metal attributes according to the polarization reflectivity ratio and the specular reflection intensity difference, identifying the morphological characteristic parameters of the abrasive particles, matching the morphological characteristic parameters with a pre-stored abrasive particle wear characteristic library, and outputting a quality evaluation report of the lubricating oil according to a matching result. The influence of oil film interference is effectively inhibited, the metal particles are accurately distinguished, and high-precision analysis on the lubricating oil abrasive particles is realized.

The invention discloses a sapphire substrate automatic defect detection and classification method based on visual inspection, and relates to the technical field of visual inspection and image processing, and the method comprises the steps: carrying out the adaptive threshold segmentation and phase consistency detection of a multi-modal image data set, and carrying out the fusion, and obtaining a joint defect probability graph; extracting defect candidate areas from the joint defect probability graph, and generating unified feature representation; inputting the unified feature representation into a double-branch neural network, performing fusion and classification through a cross attention mechanism, outputting a defect classification result, performing reconstruction by using a NeRF algorithm, outputting a defect three-dimensional shape, and generating a three-dimensional shape map; and generating a defect thermodynamic diagram based on the three-dimensional morphology map, performing space-time correlation analysis in combination with equipment process parameters, and outputting a sapphire substrate defect process analysis report. According to the method, the image contrast and the defect characterization capability are improved, so that the comprehensiveness and the sensitivity of detection are remarkably enhanced.

The invention discloses an LED chip appearance defect detection method based on image processing, and relates to the technical field of image processing, and the method comprises the following steps: S1, carrying out the multi-angle and multi-wavelength scanning of a defect-free reference LED chip under a stable exposure condition, extracting the dominant frequency vector and direction distribution information of the surface microstructure of the LED chip, and carrying out the detection of the appearance defect of the LED chip; an interference risk map is generated based on the dominant frequency vector and the direction distribution information. According to the method, dynamic adjustable optical imaging parameters and image processing results are deeply coupled, a closed-loop detection mechanism for adaptively inhibiting interference fringes is constructed, and accurate defect and interference distinguishing is realized through an interference risk map, a phase consistency index, discrimination subspace backstepping and optical adjustment, frequency domain notch and spatial domain detail recovery, so that the detection accuracy is improved. And in combination with confidence feedback and encryption sampling, the missing detection and false detection rate is remarkably reduced, and the detection stability and reliability are improved.

The invention relates to the technical field of image processing, in particular to a communication image filtering and denoising method, which comprises the following steps of: calculating a phase consistency numerical value of each pixel point in a communication image based on the input communication image, selecting an area as a structural point according to the phase consistency numerical value, and extracting a local direction attribute of the structural point, and generating a local structure feature map. According to the method, the anisotropic wavelet basis parameters are dynamically generated based on the local structure feature map, so that the wavelet function is adaptively matched with the image region characteristics in shape, direction and scale, the high-frequency detail retention capability is improved, and the problem of frequency band aliasing of a fixed wavelet basis in a non-uniform noise scene is solved. A dynamic adjustment threshold matrix is constructed in combination with local structure features and wavelet coefficient distribution, noise and effective signals are distinguished through signal saliency evaluation, wavelet coefficient amplitude is adjusted in a targeted manner, and excessive smoothness is avoided while noise is suppressed.

The invention relates to the technical field of resolution improvement, in particular to a video definition improvement method and system.According to the video definition improvement method and system, through window accumulation of adjacent frame gray difference and coordinate difference, minimum displacement is selected, mapping is updated, boundary neighborhood weighted correction is carried out, cross-frame dislocation is reduced, and motion area connection is guaranteed; capturing a three-frame gray scale sequence to calculate a difference value, writing a replacement value according to a monotone and amplitude condition, inputting the replacement value and an alignment frame difference into Fourier transform, decomposing amplitude and phase distribution in a residual field in a frequency domain, and performing amplitude and phase consistency elimination on an abnormal block during frame-by-frame superposition to obtain an abnormal block; row difference and column difference absolute values of neighborhood blocks in the residual iteration texture matrix are accumulated into energy, the energy and a center difference are combined with a threshold value to output a mark, and a texture region and an edge region are distinguished; the brightness of the low-frequency position is updated according to the weight mean value, the maximum difference and the direction difference are combined for the high-frequency position, the edge gradient is extracted through the convolutional neural network, a correction value is generated, edge steps and fuzziness are suppressed, and high-frequency details are continuously kept.

The invention belongs to the technical field of image processing, and particularly relates to a screw fastening quality evaluation method and system based on image features, and the method comprises the steps: collecting a screw fastening image, and obtaining a complex response diagram through a Log-Gabor filter; obtaining a phase congruency diagram according to each complex response diagram; acquiring gradient directions of pixel points in the phase consistency graph, and constructing an accumulator graph according to the gradient directions and offset points in a preset radius range; according to salient points in the accumulator graph and local signal-to-noise ratios of the salient points, screw positioning points are determined through two-dimensional quadratic polynomial function fitting; the torque value of the screw is obtained according to the screw positioning point driving torque measuring device, and whether the screw is fastened or not is judged. According to the method, the screw is positioned by using phase consistency in the frequency domain, so that the interference of specular reflection and extreme shadow on positioning is overcome, the accuracy of screw positioning is improved, and screw fastening quality evaluation is assisted.

The invention relates to the technical field of deep foundation pit monitoring, and discloses a deep foundation pit micro-deformation InSAR remote sensing method and system and a storage medium. The method comprises the following steps: synchronously acquiring data through a ground radar and an InSAR satellite, and obtaining a point target set and an original scattering characteristic sequence; obtaining a net deformation phase sequence without influence through registration residual correction, phase deduction and weighted fusion processing, and carrying out phase consistency quality control and unwrapping processing to obtain an accumulated deformation quantity sequence; constructing a deformation mechanism correlation characteristic matrix based on correlation analysis of the cumulative deformation quantity sequence and radar reflection intensity amplitude attenuation characteristics; and fusing the deformation mechanism correlation feature matrix and the real-time differential interference image group, extracting deformation rate and phase gradient change, and further generating a deep foundation pit local micro-deformation risk early warning index set. According to the invention, the tiny deformation of the surrounding area of the foundation pit can be effectively detected, potential risks are warned in advance, and the safety and monitoring efficiency in the construction process are improved.

The invention relates to the technical field of medical data processing, and provides a medical image data processing method based on deep learning, which comprises the following steps of: acquiring data according to an acquisition template, extracting a pulse time sequence by self-adaptive threshold peak detection, calculating an instantaneous phase according to linear interpolation, calculating a statistical magnitude, comparing a quantitative index with a preset threshold value, and calculating a pulse time sequence according to the statistical magnitude. Judging a steady state by combining a peak loss rate and an abrupt change detection rule, and calculating phase consistency between channels for verification; the method comprises the following steps of: splitting acquired data according to a concept entity to form a data relation model, implementing rapid global rigid estimation and applying affine transformation, estimating a pixel-level displacement field by adopting a pyramid dense optical flow network, applying the displacement field to an original pixel, and performing time domain fusion by taking optical flow confidence and a registration residual error as weights; and cutting the short-time image stabilization sequence after registration compensation, and outputting a pixel-level risk thermodynamic diagram, a candidate focus list and each output confidence interval by taking a hybrid network of a convolution front end and a space-time Transform backbone as a prediction model.

The invention relates to the technical field of image data processing, in particular to a numerical control lathe tool wear real-time detection method and system based on machine vision, and the method comprises the steps: positioning a region of interest of a tool image; for pixel points in the region, fusing gradient amplitudes, phase consistency and local structure anisotropy of the pixel points, and calculating an initial boundary probability; constructing an exploration path and performing weighted integration on an initial boundary probability on the exploration path so as to evaluate path consistency; and fusing the initial boundary probability and the path consistency score to obtain a final boundary probability so as to extract a wear contour and realize evaluation of the wear state of the tool. According to the method, the problem of inaccurate wear contour extraction caused by insufficient robustness in a complex processing environment is solved, and the accuracy and reliability of detection are improved.

The invention relates to the technical field of image recognition, and discloses an image recognition method for cross-domain access authentication, which comprises the following steps: acquiring a to-be-authenticated image uploaded by an image acquisition node; converting the to-be-authenticated image to a frequency domain, and extracting a plurality of response signals in multiple preset directions and scales; calculating a phase consistency measure by using the real part and imaginary part response components, and generating a phase consistency feature map; carrying out statistics on local phase distribution of the phase consistency feature map, determining a phase distribution entropy representing physical attributes of an access medium, and carrying out weight reduction suppression on the phase consistency feature map according to the phase distribution entropy; according to the method, phase interference introduced by an unnatural display medium is recognized and suppressed, in-vivo detection is achieved in identity verification, and the influence of heterogeneous imaging amplitude distortion on recognition precision is eliminated.

The invention provides a circular curve burr detection method and system based on multi-scale phase consistency and a contour model, and a medium, and the method comprises the steps: constructing a multi-scale Log-Gabor filter group, and carrying out the convolution calculation of an input image, and obtaining a response vector; calculating phase consistency measurement of each pixel point, extracting an initial edge point set, fitting an initial circular contour, performing energy minimization evolution on the initial circular contour based on an active contour model, and fitting a real contour of burrs; constructing a two-dimensional feature vector; carrying out fusion discrimination on the two-dimensional feature vectors based on a linear weighted score function, and outputting a burr detection result; according to the method, phase consistency is used as a core image feature, so that the detection algorithm has natural invariance for uneven illumination, brightness change and contrast difference, and under the scene of failure caused by poor image quality in the prior art, the contour edge can still be stably and completely extracted, and the omission ratio is remarkably reduced.

The present application relates to the technical field of image data processing, and more particularly to a numerical control lathe tool wear real-time detection method and system based on machine vision, which comprises the following steps: positioning a region of interest of a tool image; for the pixel points in the region, fusing the gradient amplitude, phase consistency and local structure anisotropy thereof, and calculating an initial boundary probability; constructing an exploration path and performing weighted integration on the initial boundary probability thereon to evaluate the path consistency; fusing the initial boundary probability and the path consistency score to obtain a final boundary probability, and extracting a wear profile therefrom to realize evaluation of the tool wear state. The present application solves the problem of inaccurate wear profile extraction caused by insufficient robustness in a complex machining environment, and improves the accuracy and reliability of detection.

The invention relates to the technical field of image segmentation, and provides a gastrointestinal tract endoscope image segmentation method, which comprises the following steps: carrying out time sequence preprocessing on an obtained intestinal tract video, eliminating fuzzy and shielded invalid frames, constructing a rhythm signal based on a multi-scale time sequence visual feature, and estimating a creeping phase; outputting a phase time sequence and a phase label of the video; selecting a representative phase key frame, performing pixel-level focus segmentation on the key frame, and calculating a pixel-level uncertainty score to obtain a key frame segmentation result with high confidence; carrying out time sequence consistency recovery on the segmentation mask of the key frame based on phase consistency mapping and phase alignment, and recovering an uninterpretable section caused by shielding with the assistance of adaptive segmentation correction of motion perception; and carrying out time sequence aggregation on a cross-frame segmentation result, extracting fragment-level statistical representation and confidence, forming a fragment-level suspicious area report, and outputting structured data for clinical auditing and index retrieval.

The application discloses a silicon carbide grain size detection method based on image processing and relates to the technical field of semiconductor material quality detection. A first feature map is generated by calculating the phase consistency distribution of a silicon carbide metallographic image to suppress scratch interference. A topological anchor sequence is identified by using the eigenvalues of a Hessian matrix, and a balanced first gradient potential field matrix is generated by combining local entropy density to suppress polycrystalline contrast fluctuation. Under the constraint of the topological anchor, an optimization evolution is performed by using a path cost function with curvature penalty and energy saturation characteristics, physical inertia closure of a grain boundary signal fracture is realized, and a closed grid vector is generated. Twin grain boundaries are identified and logically merged based on geometric parameters, and grain size detection data consistent with the physical structure of the material is output.

The invention discloses a broadband random harmonic compression analysis method based on LSTM and multi-feature fusion, and belongs to the technical field of electric digital data processing. Comprising the following steps: collecting a broadband random harmonic signal for time domain analysis, and extracting a period; judging whether each period signal can be compressed or retained or not by using LSTM (Long Short Term Memory); carrying out down-sampling and coding compression on the compressible periodic section signal; decoding and reconstructing the signal through an LSTM decoder; the decoded and reconstructed signals are preprocessed, multi-dimensional features of the signals are extracted, and evaluation indexes are normalized; calculating the compression ratio of the dynamic fusion index and the signal; and dynamically displaying the evaluation index and the dynamic fusion index. According to the method, self-adaptive decision of compression or not is realized, the compression ratio is improved, and meanwhile, the spectrum fidelity and phase consistency are also kept; and parts which do not need to be compressed are reserved as original samples, so that real-time performance and error control are both considered.

The present application relates to the technical field of coast water edge line detection, and is an island water edge line detection method based on phase consistency random walk, comprising: converting a single polarization SAR image into a phase consistency image by using a two-dimensional logarithmic Gabor filter; obtaining a sea-land membership function by using an FCM method, and creating a global sea-land priori; constructing a cross-sea bridge marker field based on LSD, and creating a marker field priori of a non-water-permeable structure connected to an island; introducing a super-pixel layer to construct a water edge line detection model; inputting the single polarization SAR image into the water edge line detection model to obtain an output label of each pixel, and realizing island water edge line detection. The present application systematically solves the problems of coherent noise interference in the single polarization SAR image, difficulty in seed point initialization of the traditional random walk method, insufficient water edge line extraction precision caused by complex island contour, and invalidity of gray scale similarity in edge detection caused by large differences in ground object backscattering, and can effectively extract the island coast line of the single polarization SAR image.

This invention belongs to the field of image data processing technology, specifically relating to a connector position offset compensation method and system based on image processing. The method includes: obtaining a connector image and all its image blocks; determining the direction dominance index of each image block, determining the highlight pixels within the image block and the distance variance of their coordinates, determining the highlight aggregation coefficient, filtering direction requirement, and the optimized number of filtering directions for each image block; determining the edge features of the connector image, determining the geometric center coordinates and deflection angle of the connector in the image, determining the position offset and angular offset of the connector in the image, and performing fine-tuning compensation. This invention, through adaptive filtering direction numbering, eliminates computational redundancy in the phase consistency algorithm, eliminates interference from highlights on edge positioning, and ensures high accuracy and real-time performance of connector position offset compensation.

The application provides a surface feature-based anti-counterfeiting information generation method, device, equipment and medium, which comprises the following steps: firstly, a microstructure intensity map is obtained by extracting surface micro-relief features through a phase consistency model; a first height map is obtained by reconstructing an initial surface height field by using a MiDaS depth estimation network; a local displacement field caused by embedding a preset mark is quantified by using a RAFT optical flow model; an initial stress disturbance distribution is constructed by fusing the height field and the displacement field as physical input of a coupling prediction model, and then the surface morphology of a concrete sample after solidification is predicted; finally, the microstructure intensity map, the local displacement field and the first height map are taken as initial surface features at the sampling time, and the initial surface features and target surface features are encoded as anti-counterfeiting information, so that the reliability of sample identification is effectively improved.

The invention discloses a phase consistency enhancement and adaptive geometric fitting corner detection method. The method comprises the following steps: firstly, carrying out adaptive histogram equalization CLAHE contrast enhancement on a cross target area image detected by YOLO; calculating a phase consistency response of the enhanced image to obtain a corner candidate region insensitive to brightness and contrast change; and calculating Harris corner response on the enhanced image, combining the Harris response with the phase consistency response score to construct a comprehensive response diagram, and screening out a corner candidate set from the comprehensive response diagram by adopting adaptive multiple thresholds. According to the method, through combination of YOLO target detection and CLAHE region enhancement, illumination non-uniformity interference such as overexposure and shadow can be effectively suppressed in a target region, a comprehensive response diagram is constructed by adopting phase consistency and Harris response in a combined manner, corner candidates are screened by using adaptive multiple thresholds, and the luminosity invariant feature and gradient significance of corners are considered, so that the accuracy and the robustness of the target region are improved. And the angular point extraction accuracy is improved.

The present application relates to the technical field of medical data processing, and provides a medical image data processing method based on deep learning, which comprises the following steps: collecting data according to a collection template, extracting pulse timing by adaptive threshold peak detection, calculating instantaneous phase by linear interpolation, comparing quantitative indicators with preset threshold values, judging steady state by combining peak loss rate and mutation detection rules, and verifying by calculating inter-channel phase consistency; splitting the collected data according to concept entities to form a data relationship model, implementing fast global rigidity estimation, applying affine transformation, estimating pixel-level displacement field by using a pyramid dense optical flow network, applying the displacement field to original pixels, and performing time domain fusion by taking optical flow confidence and registration residual as weights; cropping the short-time steady image sequence after registration compensation, taking a mixed network of a convolution front end and a space-time Transformer backbone as a prediction model, and outputting a pixel-level risk heat map, a candidate lesion list and confidence intervals of each output.

The invention discloses a self-adaptive periodic signal noise reduction method and system for an event camera and electronic equipment, and the method comprises the steps: S1, a spatial domain self-adaptive filtering step: carrying out spatial domain filtering on an original event stream output by the event camera, and generating a spatially filtered event stream; s2, a time domain density cutting step: performing event density analysis on the event stream after spatial filtering on a time axis, identifying and retaining a time interval higher than a set event density, and generating an event stream after time cutting; and S3, a pixel-level period phase filtering step: performing pixel-level period estimation and phase consistency filtering on the event stream after time cutting to generate a de-noised event stream. According to the method, the problems that the signal-to-noise ratio is too low due to high-density background noise interference when an event camera detects periodic flicker signals and an existing fixed parameter filtering method is poor in adaptability are solved, and the high-fidelity extraction capability of weak periodic signals in a complex noise environment is improved.

The invention relates to the technical field of data analysis, in particular to a fault signal phase synchronization method and device based on a data fusion algorithm. The method comprises the following steps: preprocessing a collected bearing operation data signal; the preprocessed data signals are fused, and reliable phase offset estimation of each operation period and position is calculated; the fused data signals are processed through waveform matching and a phase correction algorithm, phase deviation caused by driving speed and load changes is eliminated, and data signals after phase synchronization are obtained; and based on the data signals after phase synchronization, bearing fault features are extracted, and a phase consistency coefficient is calculated. According to the technical scheme, the identifiability of fault signal characteristics is improved through multi-sensor data fusion and an advanced phase synchronization algorithm, and an important technical means is provided for fault diagnosis of rotating machinery.

The invention discloses a fatigue test crack detection method based on thermal imaging. In the periodic fatigue loading process, the surface temperature change of a sample is detected in real time through infrared thermal imaging, and an amplitude diagram and a phase diagram of weak temperature fluctuation are obtained through calculation; segmenting the sample through local phase consistency, prefabricating a gap and a background, and only performing subsequent analysis on a sample area so as to eliminate background interference; after the amplitude diagram is binarized, the boundary displacement influence is eliminated through a simple boundary displacement review strategy and by means of the boundary displacement direction and stress concentration point position relation, complex feature point matching and displacement estimation between images do not need to be conducted through the algorithm, the calculated amount is small, and reliability is high; the crack length is calculated according to the detected movement track of the crack tip feature point, the method does not depend on static crack features, the basic assumption that sample changes inevitably come from damage generation and development is utilized, and crack length evaluation is not affected even if wrong feature points are extracted through single-frame heat map analysis.

The invention provides an anti-fake information generation method and device based on surface features, equipment and a medium. The method comprises the steps that firstly, surface micro-fluctuation features are extracted through a phase consistency model to obtain a microstructure strength diagram; reconstructing the initial surface height field by using a MiDaS depth estimation network to obtain a first height map, and quantifying a local displacement field caused by embedding of a preset identifier through an RAFT optical flow model; the height field and the displacement field are fused to construct initial stress disturbance distribution to serve as physical input of a coupling prediction model, and then the surface appearance of the concrete sample after solidification is predicted; and finally, the microstructure strength diagram, the local displacement field and the first height diagram are used as initial surface features during sampling, and the initial surface features and the target surface features are coded into anti-counterfeiting information, so that the reliability of sample identification is effectively improved.

This invention provides a method and system for detecting minute defects in proton exchange membranes (PEMs) in fuel cells. The method includes acquiring an original image of the PEM to be detected, preprocessing the original image to obtain a preprocessed grayscale image, the preprocessing including at least grayscale conversion and resolution normalization, extracting full-image phase consistency features from the preprocessed grayscale image to generate a phase consistency feature map, wherein the phase consistency feature map characterizes the structural abrupt changes of pixels in the grayscale image, and then performing multi-scale residual enhancement processing based on the preprocessed grayscale image and the phase consistency feature map to generate a defect-enhanced image. Finally, adaptive threshold segmentation and contour analysis are performed on the defect-enhanced image to locate the defect region in the original image. This method can locate the defect region in the original image of the PEM, thus overcoming the problems in the prior art.

The application discloses a kind of multi-modal satellite remote sensing image matching method and system based on phase information, first the two multi-modal images to be matched are respectively subjected to median filtering processing, to weaken the influence of noise as far as possible, then respectively on two images using phase consistency minimum moment and non-maximum suppression extraction image feature points, then using the phase consistency amplitude and angle of extension respectively constructs feature descriptor, and the Euclidean distance between feature descriptors is measured to screen matching points, finally using RANSAC algorithm to the matching point obtained is eliminated false match, obtains the final correct matching homonymic point.The application is matched based on phase information, in the matching process, using the phase consistency information of image to extract feature points and construct feature descriptor, can effectively improve the resistance of matching method to the nonlinear radiation difference between multi-modal images.

The application discloses a method for automatic defect detection and classification of sapphire substrates based on visual detection, relates to the technical field of visual detection and image processing, and comprises the following steps: performing adaptive threshold segmentation and phase consistency detection on a multi-modal image dataset, fusing the adaptive threshold segmentation and the phase consistency detection, and obtaining a joint defect probability graph; extracting a defect candidate region from the joint defect probability graph and generating a unified feature representation; inputting the unified feature representation into a double-branch neural network, fusing and classifying the unified feature representation through a cross-attention mechanism, outputting a defect classification result, reconstructing the defect classification result by using a NeRF algorithm, outputting a three-dimensional morphology of the defect, and generating a three-dimensional morphology atlas; generating a defect heat map based on the three-dimensional morphology atlas, performing space-time correlation analysis in combination with equipment process parameters, and outputting a sapphire substrate defect process analysis report. The application improves image contrast and defect characterization capability, thereby significantly enhancing the comprehensiveness and sensitivity of detection.

The invention relates to the technical field of computer vision and image processing, and discloses a magnet surface defect detection method based on computer vision. Obtaining a grayscale image, and carrying out median denoising and linear normalization; a plurality of Gabor filtering kernels are generated in a multi-scale and multi-direction self-adaption mode, and convolution is carried out on the preprocessed image to obtain a response; extracting a phase and accumulating cosine and sine components to calculate an average phase; calculating phase consistency based on the phase deviation; performing adaptive threshold segmentation to generate candidate masks; carrying out 8-neighborhood connected domain analysis on the mask and filtering small-area noise; and calculating a bounding box, a centroid and an area of each effective connected domain and outputting a report. Through Gabor phase consistency measurement and self-adaptive segmentation, the problems of process fragments, parameter mismatch, missing and false detection and the like are solved, high robustness and accuracy are achieved, and the method is suitable for industrial automatic quality inspection.