24074 results about "Imaging processing" patented technology

The invention discloses a high-precision image processing method and system based on illumination adaptive compensation, and relates to the technical field of computer vision and image processing, and the method comprises the steps: inputting an original image, and dividing the image into a high-frequency edge layer, an intermediate-frequency texture layer and a low-frequency illumination layer through a multi-scale residual network; acquiring illumination intensity, color temperature and scene categories in real time by using an ambient light sensor and a scene semantic segmentation model, and generating dynamic compensation parameters; carrying out dynamic range expansion on a low-frequency illumination layer based on a physical illumination model, and adjusting the weight of highlight suppression and dark area enhancement through a self-adaptive S-shaped exposure curve; a double-branch generative adversarial network is adopted, noise suppression and super-resolution reconstruction are carried out on the high-frequency layer, and texture detail enhancement is carried out on the intermediate-frequency layer; aligning the data of the depth camera and the infrared sensor with the visible light image through a cross-modal fusion module; and performing tone mapping on the fused image based on human visual characteristics, and outputting an enhanced image with a high dynamic range and reserved details.

A pattern which does not appear at a flat portion in normal binarization processing is set as a code pattern, and a code formed from this pattern is attached. At this time, code attachment with little degradation in image quality is implemented by selecting an unnoticeable pattern.

The invention relates to the technical field of image processing, and discloses an industrial part alignment method and system based on visual analysis and a storage medium. The method comprises the steps that a three-view camera collects an industrial part image, and preprocessing is carried out through gradient magnitude local contrast enhancement to obtain an enhanced image; performing hierarchical feature extraction to identify edge contours and key control points to form a multi-dimensional feature set; and establishing a dynamic reference coordinate system based on the feature set to obtain a part space attitude matrix. And the attitude deviation is compensated through Z-axis offset and rotation coupling error analysis. Posture adjustment is decomposed into a plurality of sub-stages, an alignment track is optimized by adopting a variable speed planning strategy, and accurate alignment of the parts is achieved. The problems that multi-view visual information fusion is insufficient, a special recognition algorithm for geometrical characteristics of the industrial parts is lacked, and Z-axis offset and rotation coupling error compensation is inaccurate in the posture adjustment process are solved, and the precision and stability of alignment of the industrial parts are improved.

The invention discloses an image processing method and device and a storage medium. The method comprises the steps of obtaining a to-be-simulated 3D convolution model and training data; decomposing the 3D convolution model into cascading of a 3D space convolution model and a 3D time convolution model to obtain a pseudo 3D cascading convolution model; training a pseudo 3D cascade convolution modelby using the training data, and obtaining parameters of a 3D spatial convolution model and a 3D time convolution model; converting the 3D space convolution model and the 3D time convolution model intoa 2D space convolution model and a 2D time convolution model; setting a feature rearrangement rule for the 2D spatial convolution model and the 2D time convolution model; mapping model parameters ofthe 3D spatial convolution model and the 3D time convolution model into parameters of a 2D spatial convolution model and a 2D time convolution model to obtain a 2D cascaded convolution model; and performing convolution operation on the image by using the 2D spatial convolution model and the 2D time convolution model. By means of the mode, image processing conducted through 3D convolution operationcan be achieved through the 2D convolution model.

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.

The invention discloses an alimentary canal pathological diagnosis visual language large model construction method based on reinforcement learning and application thereof, and belongs to the technical field of medical image processing. The method comprises the following steps: firstly, extracting pathological information through layout analysis and adaptive threshold processing, and recombining the pathological information into a structured data set containing an inference chain; secondly, a visual encoder and a multi-branch classifier are used for extracting features and confidence coefficients, and dynamic structured cue words are generated; and finally, inputting the image and the cue word into a multi-modal large model, carrying out supervised fine-tuning hot start, and carrying out reinforcement learning training by adopting a group relative strategy optimization algorithm in cooperation with a composite reward function containing format, semantics and diagnosis dimensions. The problems that a general model is prone to generating illusion in a pathological scene and lacks reasoning logic are solved, and the accuracy and logicality of pathological report generation are remarkably improved.

The invention relates to the technical field of industrial machine vision detection and image processing, in particular to an automobile part production mold surface smoothness detection system based on image enhancement, which comprises a data acquisition module used for acquiring an original grayscale image of the surface of an automobile part mold; performing low-pass filtering processing on the original grayscale image to eliminate imaging thermal noise; the manifold reconstruction module is used for constructing a structure tensor field; reversely deducing a pseudo-curvature field of the mold surface; the adaptive enhancement module is used for generating a corrected image; constructing a texture orthotropic diffusion model; generating a texture reconstruction reference image; the surface metering module is used for calculating the difference between the corrected image and the texture reconstruction reference image and generating a defect saliency image; calculating the surface roughness value of the mold surface; according to the method, the problem that design textures and abnormal scratches are difficult to distinguish in the prior art is effectively solved, and the technical bottleneck that micro defects are easily missed in a complex geometric structure in traditional visual detection is overcome.

The invention discloses a transformer equipment oil leakage monitoring method and system based on image recognition, relates to the technical field of image processing, and effectively eliminates static background interference by constructing a standardized image sequence S (t), performing gray gradient analysis and pixel feature extraction, recognizing candidate oil spot areas and constructing a disturbance active score Sx. The edge structure and the morphological stability coefficient of the dynamic oil stain suspected area are further extracted, and the credible level quantitative judgment of the dynamic oil stain suspected area is realized by integrating the characteristic indexes and the morphological stability coefficient, so that the dynamic identification capability and the anti-interference capability are realized; the oil leakage identification accuracy and the system automation risk response capability in a complex environment are improved, false alarm and missing alarm are effectively avoided, and the operation safety of equipment is guaranteed.

The invention provides a multi-view-oriented three-dimensional scene image reconstruction registration and optimization method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining multi-frame three-dimensional scene image data, extracting a multi-level feature set, constructing a cross-view-angle semantic association graph, building a feature corresponding relation, and calculating a multi-view-angle spatial transformation relation parameter. Performing coordinate system alignment on the image data to generate an initial three-dimensional reconstruction result, and performing optimization in combination with a multi-target joint optimization function and a dynamic adaptive weight regulation and control mechanism. According to the method, the precision and robustness of three-dimensional scene reconstruction are improved, and the problem of registration errors caused by large view angle difference in a complex scene is solved.

The invention discloses an industrial part defect sample accurate generation method based on a conditional diffusion model, and belongs to the field of image processing and artificial intelligence. The method forms a closed-loop cooperative system by constructing four deep coupling modules of physical constraint noise scheduling, multi-scale feature coupling, double-domain feedback optimization and adaptive weight adjustment; a defect physical forming mechanism is converted into a dynamic noise scheduling strategy, deep interaction between condition information and a feature map is established at multiple levels of a diffusion network, quality closed-loop optimization is achieved through dual evaluation of a pixel domain and a frequency domain, and training weight is dynamically adjusted according to defect scarcity. And multi-scale accurate control is realized, a quality guarantee closed loop is established, the problem of data imbalance is effectively solved, and the performance of an industrial defect detection model is remarkably improved.

The invention discloses a dark light enhancement method under the view angle of an unmanned aerial vehicle, and relates to the technical field of image processing and enhancement, and the method comprises the steps: collecting a continuous frame dark light image sequence of a target when the unmanned aerial vehicle flies, carrying out the preprocessing operation including denoising and normalization, and forming a dark light image set; and estimating the motion between adjacent frames by using an image recognition algorithm. According to the invention, through dynamic range compression and detail enhancement processing, the image definition and visibility in a dark light environment are improved, the brightness difference of the image is balanced by adopting a dynamic range compression algorithm, local overexposure or underexposure is avoided, the image can keep a good visual effect under different illumination conditions, and the image quality is improved. And the detail enhancement processing highlights texture and edge information in the image through multi-scale gradient fusion and adaptive sharpening, and improves the detail definition in dark light, so that the unmanned aerial vehicle can recognize a target more clearly when executing a task at night or in a low-light environment, and the task execution efficiency and accuracy are improved.

The invention relates to the technical field of image processing, in particular to an image acquisition and analysis method and system, and provides the following scheme: obtaining a visible light and near-infrared multispectral image, generating a spectral difference image, and performing weighted fusion to obtain a first image; segmenting a target region based on the fused saliency map, and calculating a pixel reflectance ratio; solving a color mapping matrix according to the reflectance ratio, and carrying out color correction on the target region to obtain a standardized feature image; and extracting characteristic parameters such as spectrums, colors and textures, inputting the characteristic parameters to a multi-branch convolutional neural network, fusing the characteristic parameters through an attention mechanism, and outputting a state classification result and a quantitative index. The cross-spectral imaging difference can be adaptively compensated, and the fusion precision and the analysis stability are improved.

The invention discloses an image enhancement method for brightness gain self-adaptive regulation and control under low illumination, and relates to the technical field of image data processing. The image enhancement method for brightness gain adaptive regulation and control under low illumination comprises the following steps: S1, acquiring original image data and auxiliary processing data under a low illumination environment for preprocessing, and constructing an image processing database after storing the original image data and the auxiliary processing data; s2, performing illumination distribution optimization and brightness correction operation through brightness deviation analysis, and outputting an illumination enhancement component; s3, performing de-noising and detail enhancement processing according to a noise color deviation result, and outputting a corrected reflectivity component; s4, through image quality enhancement analysis, using a loss function to guide a double U-Net architecture to carry out detail reconstruction and color optimization on the image; and S5, based on the quality feedback and the difficult sample, adjusting processing parameters and driving algorithm closed-loop optimization. The problems of insufficient image brightness, noise interference, color distortion and detail missing multiple degradation in a low-light environment are solved.

The invention relates to the technical field of image processing, and discloses a packaging material printing quality detection method and system based on machine vision. The method comprises the following steps: acquiring a multispectral image sequence and three-dimensional shape data of a moving packaging and printing material under different illumination, and constructing a dynamic three-dimensional physical attribute field; generating a virtual reference image and a dynamic reference image, constructing a multi-modal reference image, carrying out space-time registration on the multi-modal reference image and the dynamic three-dimensional physical attribute field, and calculating the difference between the multi-modal reference image and the dynamic three-dimensional physical attribute field in different dimensions to generate a multi-dimensional difference quality field; each dimension difference is enhanced through local statistics, and the comprehensive defect confidence coefficient is calculated based on the enhanced dimension difference; and extracting a defect region based on the comprehensive defect confidence, generating a defect evolution sequence and a defect track, analyzing defect track characteristics, constructing a correlation model in combination with process parameter time sequence data of the printing equipment, and positioning a defect reason. According to the invention, high-precision, multi-dimensional and self-adaptive printing defect detection and traceability can be realized.

The invention discloses a short video network public opinion information identification method based on an image processing technology, and relates to the technical field of artificial intelligence and image processing, and the method comprises the following steps: S001, through obtaining image frames, audio tracks and time sequence information of a short video, constructing a multi-modal fusion model, extracting continuous image frames with suspicious identity features, and carrying out the recognition of the short video network public opinion information; generating a forgery risk area distribution map; and S002, performing semantic consistency verification according to the counterfeit risk region distribution map, and extracting space and time anomaly features existing among facial micro-expressions, pronunciation actions and background semantics in the image frame. According to the method, a multi-modal model is constructed by fusing image, audio and time information, fine abnormal features, traceability forgery starting points and propagation paths in a deep forgery video are identified, and an identification strategy and a public opinion response mechanism are dynamically adjusted, so that accurate identification, adaptive processing and closed-loop control of short video public opinion risks are realized; and the identification accuracy and the treatment efficiency are improved.

The invention discloses a shield muck volume estimation method and system based on image processing, and particularly relates to the technical field of tunnel engineering monitoring, and the method comprises the steps: obtaining muck RGB and depth images through an image collection device, generating high-precision three-dimensional point cloud data through the combination of laser scanning and a multispectral technology, and calculating the muck volume through a slicing method. And fusing the mass flow and water content data of the belt weigher, dynamically generating a calibration factor, and correcting a volume estimation result in real time. The system comprises an image acquisition module, a point cloud processing module, a volume calculation module and a dynamic calibration module, has the characteristics of automation, high precision, real-time feedback and the like, effectively solves the problems of large error and slow response of a traditional manual metering and single weighing mode, and is suitable for continuous and accurate monitoring of the volume of muck in the shield construction process.

The invention belongs to the field of computer vision and industrial defect detection, and discloses a steel coil end face defect detection method and system based on CBAM-BiFPN and multi-loss optimization, and the method comprises the steps: image preprocessing and region extraction: extracting a steel coil end face region through OpenCV and other image processing methods; dividing the high-resolution image into a plurality of small blocks and performing data enhancement; the method comprises the following steps: constructing a YOLOv11 network based on CBAM-BiFPN, introducing a CBAM attention mechanism and BiFPN feature fusion structure, and constructing an improved YOLOv11 detection network; multi-loss function joint optimization training is carried out, and model training is carried out in combination with loss functions such as Focal Loss and WIoU; and fusion of detection results and defect reconstruction output: reconstructing original image defects of all tile image detection results in a space coordinate mapping and redundant region fusion mode, and realizing high-precision overall detection output. Compared with a traditional method, the method still has the high recognition capability in a complex background and low-contrast scene, the detection precision and stability are obviously improved, and higher industrial adaptability and practical value are achieved.

The invention relates to the technical field of transformer substation intelligent patrol, and provides a transformer substation three-dimensional fusion patrol method and system based on digital twinborn and autonomous identification. According to the method, a fused three-dimensional model is constructed through multi-source data acquisition and a three-dimensional Gaussian splash algorithm, and in combination with deep learning-based point cloud semantic segmentation and clustering, an equipment-patrol means coverage relationship is generated. Creating a virtual inspection proxy object based on a three-dimensional virtual environment, and controlling terminals such as an unmanned aerial vehicle to collect real-time video image data; the system carries out automatic identification on pictures, automatically completes equipment level alignment and standard point location identification, generates fine control of camera zooming, horizontal rotation, pitching and the like, and realizes standardized view finding and acquisition. By combining an enhanced recognition algorithm, traditional image processing and a deep learning model are fused, model self-evolution is realized through incremental learning, flexible expansion and collaboration of various patrol terminals are supported through a unified interface, and refined, real-time and intelligent patrol operation and maintenance requirements of an intelligent substation are met.

The invention relates to the technical field of medical image detection, and discloses a medical image quality detection method based on image processing. The method comprises the following steps: acquiring medical image data to be detected, wherein the medical image data comprises a multi-modal scanning image sequence and corresponding acquisition parameters; the medical image data are preprocessed, standardized image data are generated, and the standardized image data comprise unified parameters of spatial resolution, gray scale range and noise level; extracting structural features of the standardized image data, wherein the structural features comprise tissue boundary gradient distribution, texture consistency and local contrast information; constructing a quality evaluation model according to the structural features, wherein the quality evaluation model analyzes a mapping relationship between the structural features and preset quality indexes through a dynamic convolutional network; and outputting a quality defect detection result based on the quality evaluation model, wherein the quality defect detection result marks an image region with artifacts, fuzziness or distortion.

The invention relates to the technical field of target tracking, in particular to a photoelectric tracking algorithm and system for self-adaptive target tracking, and the system comprises an image collection module, a cross-spectrum self-adaptive sensing module, an image processing and target recognition module, a self-adaptive tracking algorithm module and a servo control module, and constructs a closed-loop feedback link to achieve the synchronous optimization of parameters in a whole link. The algorithm comprises the steps of image acquisition and adjustment, target detection, parameter optimization, servo driving and feedback. The system can accurately estimate a high-speed turning target, is adaptive to complex environments such as low illumination / haze and the like, completes sheltered target recapture within 0.5 s, has servo compensation precision of + / -0.1 degree, is suitable for airport bird monitoring, port unmanned aerial vehicle tracking and highway vehicle monitoring scenes, and significantly improves tracking stability and practical value.

The invention belongs to the technical field of image processing, and particularly relates to a titanium cylinder inner wall defect detection method and system.The method comprises the steps that a titanium cylinder inner wall image is collected, and a highlight area and boundary pixel points thereof are extracted through threshold segmentation and corrosion operation; calculating a scale adjustment factor by combining a highlight area proportion and a boundary gradient amplitude, dynamically scaling the reference kernel according to the scale adjustment factor, and determining a standard deviation of a multi-scale Gaussian kernel; constructing a Gaussian kernel function of each scale, and calculating the sum of difference values of the titanium cylinder inner wall image and convolution results of all scales in a logarithm domain to obtain an enhanced titanium cylinder inner wall image; and performing threshold segmentation on the enhanced titanium cylinder inner wall image to identify a defect area. According to the method, the filtering scale is adjusted in a self-adaptive mode, mirror reflection interference is effectively restrained, halo artifacts are eliminated, and tiny defects covered by highlight are accurately recognized.

The invention relates to the technical field of image compensation, in particular to a reflection compensation method based on image processing, and provides the following scheme: acquiring an environment panoramic image by using a panoramic camera to establish a scene global coordinate system, and acquiring a multi-view original image in combination with a camera array arranged in the circumferential direction of an object; determining a rotation symmetry axis according to the multi-view contour features, reconstructing a three-dimensional geometric body, and calculating normal distribution and curvature change; generating a prediction image based on diffuse reflection and specular reflection hypothesis in the surface expansion domain through virtual visual angle disturbance, identifying a reflection region according to color and gradient consistency, and generating a reflection mask; and performing texture reconstruction on the reflective area by using geometric registration and multi-view compensation, and finally performing splicing and fusion to obtain a non-reflective high-fidelity panoramic image. The method does not need to change the field illumination condition, and can achieve the precise recognition and compensation of the complex curved surface reflection in the cultural relic in-situ collection environment.

The invention discloses a cross-modal eye fundus image generation method and system based on a generative adversarial network, relates to the technical field of medical image processing, and constructs an eye fundus focus perception and edge consistency generative adversarial network by taking a cyclic consistency generative adversarial network as a baseline. The core of the method is that a lesion perception mixed attention module is embedded in a bottleneck layer of a generator so as to strengthen the extraction capability of fine features of a lesion area; an edge information extraction module is designed, and key edge features are accurately extracted in combination with Roberts edge detection, wavelet transform and non-local mean denoising; and a joint loss function containing edge consistency loss is constructed, and the semantic consistency of a focus structure during cross-modal generation is ensured by minimizing the feature difference between the source image and the generated image. According to the method, the problems of disordered content, inconsistent structure and unstable training of the generated image in the prior art are effectively solved, and the simulation degree and clinical availability of the generated image are remarkably improved.

The invention discloses a road cavity volume calculation method and device based on a three-dimensional FK migration algorithm, equipment and a medium, and relates to the technical field of road cavity detection, and the method comprises the steps: obtaining three-dimensional ground penetrating radar data, and carrying out the preprocessing of the data, and obtaining target radar data; converting the target radar data to a frequency wavenumber domain to obtain frequency wavenumber domain radar data; establishing a road hierarchical velocity model, and performing hierarchical wave field extrapolation and double weight correction based on the frequency wave number domain radar data and the road hierarchical velocity model to obtain corrected wave field data; performing sparse constraint optimization imaging processing on the corrected wave field data to obtain a target imaging result; and carrying out boundary extraction processing on the target imaging result to obtain a three-dimensional cavity boundary point set, and carrying out voxelization processing based on the three-dimensional cavity boundary point set to obtain a road cavity volume. According to the invention, high-precision restoration of the shape of the road cavity and automatic extraction of the volume can be realized in three-dimensional GPR imaging.

The invention discloses an unmanned aerial vehicle inspection method and system applied to foundation pit accumulated water monitoring, and the method comprises the steps: determining an optimal route node sequence, and generating an unmanned aerial vehicle control instruction set; acquiring internal and external parameters of a multi-source sensor, and performing time alignment compensation on image frames and sensor data; acquiring a ponding area image coordinate set and attribute data; a three-dimensional projection point set of the ponding area is obtained, and the real coverage area of the ponding area is calculated; generating a three-dimensional overview map of the foundation pit by using the multi-view image of the foundation pit area; determining a three-dimensional coordinate point set and an area estimation result of the ponding area; and projecting the three-dimensional coordinate point set of the ponding area to the three-dimensional overview map of the foundation pit to generate a hidden danger monitoring report. By integrating the unmanned aerial vehicle, the sensor data and the advanced image processing technology, the accuracy, stability and visualization effect of foundation pit accumulated water monitoring are greatly improved, more reliable and comprehensive technical support is provided for safety management of foundation pit engineering, and the safety risk in the construction process is remarkably reduced.

The invention relates to the field of image processing, and discloses an object monitoring method and device based on multi-source data and a storage medium, and the method comprises the steps: synchronously collecting multi-channel video streams, environment parameters and equipment position information, and carrying out the time alignment and data association processing, and forming associated data; performing feature matching on the multiple paths of video streams, fusing position information and environment parameters, and establishing a mapping relation between video feature points and a unified space coordinate system; splicing the multiple paths of video streams in real time according to the mapping relation to generate a panoramic video stream; performing dynamic scene analysis based on the panoramic video stream and the environmental parameters, and identifying a target type and a state to obtain an analysis result; and generating an equipment regulation and control strategy based on the analysis result, and generating and issuing a regulation and control instruction for adjusting the working parameters of the multi-view image acquisition device based on the equipment regulation and control strategy. According to the invention, the splicing precision, the real-time performance and the target identification accuracy of panoramic monitoring can be improved, and the dynamic adaptive regulation and control of the equipment can be realized.

The invention provides an image processing method and device for substation equipment and electronic equipment, and relates to the field of image recognition. In the method, a fusion image sequence is generated through multi-source disturbance space-time tensor modeling and nonlinear image attenuation correction, features are extracted in combination with a pulse modal attention mechanism, a refined segmentation mask is generated, illumination compensation and texture reduction are completed in a strong reflection material scene, and geometric return is further performed by using electrical topology and a BIM model. And finally, outputting a return image sequence and an identification result under a standard view angle. By implementing the technical scheme provided by the invention, the problems of image processing degradation and distortion of the substation equipment can be solved conveniently, so that the accuracy of defect identification and classification is improved.

The invention belongs to the technical field of image processing, and particularly relates to a time sequence frequency domain feature enhanced diffusion-based sea temperature image completion method and system, and the method comprises the following steps: inputting a damaged sea temperature image, an initialized cloud mask, a weekly average sea temperature image and a historical sequence sea temperature image into a time sequence frequency domain feature extraction module for processing; and mapping into fused frequency domain features, and outputting a complex frequency domain condition vector. A real SST image on the current day is coded into an initial latent variable through a latent space enhancement diffusion module, a complete noisy latent variable is generated through forward diffusion sampling, a denoised latent variable is obtained through backward stable diffusion sampling, after the denoised latent variable is decoded into a pixel field through an output reconstruction module, constraint post-processing is carried out in combination with a mask and a damaged sea temperature image, and a real SST image is obtained. And finally outputting a reconstructed image. And the damaged sea temperature image completion precision and the time sequence continuity are improved.

The invention discloses a liver and gall lesion early screening system and method based on image fusion, and relates to the technical field of medical image processing and computer-aided diagnosis, and the method comprises the following steps: reconstructing a multi-modal image space-time coordinate system under a unified event time baseline, generating a respiratory displacement field and a magnetic sensitive pulse fingerprint, and constructing an artifact suspicion map; and performing anti-fact playback based on the artifact suspicion chart, performing frame-by-frame playback on the image acquisition sequence, quantifying artifact superposition tracks with consistent directions, and solidifying an artifact anchor point set. According to the method, space-time coordinates are constructed based on a unified event time baseline, a breathing displacement field and magnetic sensing pulse fingerprints are introduced, anti-fact playback, distortion kernel inference and residual decoupling are combined, artifact recognition and fusion intervention are achieved, and artifact closed-loop elimination is completed by judging threshold-driven fusion regulation and time reversal phase gating, so that the artifact recognition accuracy is improved. And the fused image authenticity is improved.

The invention belongs to the related technical field of brain image processing, and provides an Alzheimer's disease classification method and system based on topology perception and a group hypergraph in order to solve the problem of inaccurate classification of the Alzheimer's disease in the prior art. Constructing a dynamic function connection network sequence through a sliding window strategy; a local topology perception encoder and a global topology perception encoder are respectively used for extracting local topology features and global topology features of each time window, deep interaction and fusion are carried out, and comprehensive feature representation of a tested level is generated; according to the method, each subject is used as a hypergraph node, hyperedges are constructed on the basis of comprehensive feature representation of a subject level and by combining feature similarity calculated by diffusion tensor imaging features and clinical embedded features of the subject, then a group hypergraph is constructed, a classification result is obtained by using a hypergraph neural network, and the early classification diagnosis accuracy of the Alzheimer's disease is effectively improved.