556 results about "Computer vision and image 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.

The invention discloses an unmanned aerial vehicle multi-modal feature fusion target tracking method and system based on natural language description, belongs to the technical field of computer vision and image processing, and solves the problem that in the prior art, when the quality of an image collected by an unmanned aerial vehicle is poor or image features are not obvious, the target tracking capability and the long-time tracking capability are poor. Natural language description is carried out on a traffic accident scene in an image of an unmanned aerial vehicle visual angle, and a language prompt is obtained; constructing a scene-context feature pyramid network to perform context information enhancement processing on the image of the view angle of the unmanned aerial vehicle to obtain a feature-enhanced image; respectively carrying out visual coding and language coding on the enhanced image and language prompt to obtain visual features and language feature vectors, and carrying out visual-language bimodal feature local alignment; and fully fusing the obtained aligned new language features with the visual features to obtain multi-modal features for target tracking. The method is used for multi-modal feature fusion target tracking of the unmanned aerial vehicle.

The invention relates to the field of computer vision and the field of image processing, in particular to a blind guiding scene recognition method based on a multi-modal visual large model. Comprising the following steps: data acquisition: acquiring image or video data of a typical blind guiding scene, marking, constructing a data set, introducing an image prompting mechanism, and dividing a training set and a test set; constructing a multi-modal visual large model which comprises an image coding module, a semantic prompt coding module, a visual language fusion module and a scene analysis semantic decoding module; the model is tested and optimized, and a multi-task loss function is adopted for optimization; a blind guiding auxiliary system is constructed, image acquisition, visual understanding and voice broadcasting functions are integrated, and a closed-loop process is realized. According to the method, the problems of strong target dependence, poor universality and weak semantic comprehension ability of the existing blind guiding identification technology are solved, accurate identification and semantic feedback of complex scenes are supported, and the intelligent level and the environment adaptability of a blind guiding system are improved.

The invention discloses a shielding object moving and editing method and system based on a diffusion model, and belongs to the technical field of computer vision and image processing. Comprising the following steps: acquiring an original image and a visible area mask and performing preprocessing to generate a standard input image and a standard mask; processing the standard input image and the standard mask through a de-occlusion branch, guiding a diffusion process by using a cross attention mechanism and a self-attention mechanism, and generating a de-occlusion object image; and obtaining a target position, inputting the original image, the visible area mask and the target position into a moving branch for processing, introducing a potential space adjustment operation, and generating a shielding object moving image under the guidance of a local text condition. According to the method, the integrity and authenticity of the shielded object after editing can be kept, natural fusion of the target position and traceless reconstruction of the original position are realized, and the problems of insufficient content completion, unnatural fusion of the target position and residual artifacts of the original position in the prior art are solved.

The invention provides a panoramic image three-dimensional reconstruction method based on 3DGS, and relates to the technical field of computer vision and image processing, and the method comprises the steps: 1, collecting a panoramic image sequence of a to-be-reconstructed scene, carrying out the unified coding through equidistant cylindrical projection, and obtaining a coding data set; and step 2, executing panoramic motion recovery structure processing on the coded data set, and generating six-degree-of-freedom camera pose parameters of each image and a corresponding sparse three-dimensional point cloud. According to the invention, high-precision and high-efficiency panoramic scene three-dimensional reconstruction is realized, a high-quality panoramic image or a dense three-dimensional model can be output, and the three-dimensional reconstruction quality and efficiency are improved.

The invention discloses a body-in-white welding seam recognition and automatic welding method based on a machine vision technology, particularly relates to the technical field of computer vision and image processing, and is used for solving the problem of welding seam track recognition accuracy caused by insufficient processing capability of an existing three-dimensional vision recognition method on incomplete and uncertain point cloud data. Through the steps of multi-view point cloud acquisition and registration, probabilistic confidence evaluation, region growth of track continuity constraint, multi-track fusion optimization and the like, accurate identification of a body-in-white welding seam track under a complex working condition is realized; firstly, multi-view point cloud data are obtained, probabilistic registration is carried out to generate a confidence evaluation result, then candidate tracks are generated based on confidence weighting and semantic constraint, finally, an optimal track is generated through intelligent optimization and converted into a welding instruction which can be executed by a robot, and the accuracy and robustness of weld joint recognition are effectively improved.

The invention discloses an image feature matching optimization method based on intra-class space consistency in the technical field of computer vision and image processing. The method comprises the following steps: feature point extraction and preliminary matching: extracting feature points from a query image and a reference image and performing preliminary matching; initialization and transformation model estimation: initializing a matching point set and a residual error, and calculating an initial transformation model; error calculation and matching point set updating: calculating the error of the matching point pair, and updating the matching point set by adopting a dynamic screening method; performing residual optimization: judging whether the optimal condition is reached or not based on the residual, and deciding whether to continue iteration or not; performing intra-class space consistency clustering and isolated cluster elimination: performing clustering analysis after the optimal residual error is obtained, and eliminating isolated clusters based on an intra-class space consistency separation ion structure; and outputting a result: outputting a matching point set after the isolated clusters are removed. The method solves the problem that a traditional feature matching method is difficult to completely remove mismatching in a complex scene and is sensitive to noise.

The invention relates to the technical field of computer vision and image processing, in particular to a robust image matching system and method based on multi-scale attention feature fusion, and the system comprises a feature extraction module which extracts a multi-resolution feature map containing image features from a to-be-matched image; the position coding module performs spatial position coding on the image features to improve the spatial perception capability of the model; the multi-scale attention module performs multi-scale enhancement processing on the image features, so that the model is more effectively focused on the significant part of the image; the attention module establishes a dependency relationship between image features; the coarse-level matching layer module generates a coarse-level matching result and screens out a matching pair with relatively high confidence coefficient; and the coarse-to-fine module carries out fine processing on the coarse matching result, so that more accurate feature matching is realized. According to the method, the effectiveness and reliability of the method are verified by testing the image matching performance of the to-be-tested picture under the conditions that the to-be-tested picture contains different illumination changes, extreme visual angle changes, repeated textures and the like.

The invention discloses a morphological gradient region replacement method based on SAM semantic segmentation and user guidance, and relates to the technical field of computer vision and image processing, and the method comprises the steps: carrying out the semantic segmentation of a to-be-processed image through an SAM model, extracting a multi-level semantic feature, carrying out the standardization and dimension reduction, extracting a causal factor based on independent component analysis, and carrying out the user guidance. A directed causal factor association graph is generated through Granger causal relationship test, and a causal attribution probability graph is generated through reverse mapping; constructing a structured causal graph, and generating a causal mask through a graph convolutional network; encoding the original interaction signal into a guide thermodynamic diagram; constructing a diffusion equation, forming a gradual change control equation by dynamically fusing and guiding the intensity distribution of the thermodynamic diagram and an image semantic diffusion item, and iteratively solving the gradual change control equation; generating an anisotropic morphological operation kernel according to the geometric curvature characteristics of each region in the replacement mask; and fusing the optimized replacement mask with the target content based on a gradient domain optimization algorithm to generate a gradient replacement image.

The invention discloses a road video event rapid detection method based on edge calculation, particularly relates to the field of computer vision and image processing, and is used for solving the problem of motion evidence distortion caused by frame-level sequential disorder and content gaps in a road monitoring video. The method comprises the following steps of: reading a video clip, constructing a time sequence correction graph according to inter-frame content similarity and boundary continuity, rearranging to generate a corrected sequence, outputting a frame consistency index, estimating a stable motion field and a continuity score on the corrected sequence, and writing back an image stabilization amplitude correction time anchor point; generating an event evidence sequence according to the stable motion field, combining track breakpoints according to a time sequence correction graph, initiating backtracking revision to return candidate event segments, and if consistency verification is executed on candidate events, performing cross-level revision on the time sequence correction graph and image stabilization amplitude to trigger the stable motion field to re-estimate and output a confirmation event; and issuing a trigger result and establishing a mapping index as a new fragment to initialize priori acceleration time sequence correction and judgment convergence.

The invention belongs to the field of computer vision and image processing, and relates to fine-grained action classification of a picture sequence after video framing by adopting a deep convolutional neural network, in particular to a fine-grained video behavior recognition method based on double-text prompt. The method is simple in program and easy to implement, actions capable of recognizing the fine grit of the human body can be obtained, text description can be divided into different fine grits through a large language model for the fine grit actions of the human body, and then the generated text feature vectors and video features of different time scales are subjected to cross attention mechanism response, so that the recognition accuracy of the human body is improved. Unique details of human body motion in a video can be better found, so that fine-grained motion is more accurately reasoned.

The invention belongs to the technical field of computer vision and image processing, and discloses a remote sensing small sample target detection method based on double-attention guided transfer learning, and the method comprises the steps: obtaining a remote sensing image data set, and carrying out the preprocessing; taking the preprocessed remote sensing image training set as input, constructing a basic detection model by using a ResNet-101 backbone network, a feature pyramid network and a content awareness upsampling and regional proposal network, and obtaining basic model parameters; basic model parameters are used as input, a DA-FSDET network is trained based on a content awareness strip pyramid and a deformable attention area proposal network, and the trained DA-FSDET network is used to acquire a category detection frame containing small sample categories and confidence. Through cascading and cooperative work of the content awareness stripe pyramid and the deformable attention area proposal network, the detection precision and robustness of the multi-scale target in the remote sensing image are effectively improved.

The invention relates to the technical field of computer vision and image processing, in particular to an infrared polarization image super-resolution method based on a cross attention double-branch network, and the method comprises the steps: obtaining infrared polarization images with two different resolutions in the same scene through two infrared polarization cameras with different image resolutions; constructing a double-branch super-resolution network model, training the double-branch super-resolution network model according to the multiple pairs of image data sets until a robust fitting state is reached, and obtaining the trained double-branch super-resolution network model; and obtaining a high-resolution infrared polarization image from the low-resolution infrared polarization image to be subjected to super-resolution through the double-branch super-resolution network model. According to the method, the infrared polarization intensity image and the infrared polarization degree image are subjected to super-resolution at the same time through the deep learning model, the features are fused mutually, and high-quality super-resolution image reconstruction is achieved.

The invention relates to the field of computer vision and image processing, and discloses a remote controller liquid crystal screen image detection method based on computer vision, which comprises the following steps: S1, acquiring an original image containing a remote controller, and carrying out gray level conversion and filtering denoising processing on the original image to obtain a preprocessed image; s2, calculating the local information entropy and the change rate of the image through a sliding window under multiple scales based on the preprocessed image, generating a multi-scale entropy gradient heat map, and screening out a candidate region with high entropy difference as a liquid crystal display region according to a set threshold value; and S3, carrying out vectorization partitioning on the candidate region, and constructing an image matrix. According to the invention, by introducing an image preprocessing mode of low-rank sparse decomposition and normalization processing, the expression ability of structural information in the character image is effectively enhanced, and the feature extraction accuracy under the conditions of complex background, low character contrast and the like is improved.

The embodiment of the invention provides an image rain removal method based on a multi-scale state space model, and belongs to the field of computer vision and image processing. The method comprises the following steps: acquiring an image with rain stripes, and decomposing the image into a multi-scale pyramid image through down-sampling; respectively inputting the image with the rain stripes and the multi-scale pyramid image into a pre-constructed multi-scale state space model, and performing feature extraction and image reconstruction through a convolutional layer and an encoder-decoder network to obtain a reconstructed residual image; and adding the image with the rain stripes to obtain a target rain-removed image. According to the method, global information is modeled by using a state space model, multi-scale complementary information is effectively utilized in combination with a multi-scale framework, cross-scale complementary features are explicitly mined, rain stripes are more accurately removed, a high-quality clear image is gradually recovered from a rain image, and the definition and visual quality of the rain-removed image are ensured.

The invention discloses a three-dimensional scene reconstruction method and device, and belongs to the field of computer vision and image processing. The method comprises: determining spatial geometric information of a shooting scene according to M two-dimensional images, the M two-dimensional images being images of the shooting scene at different shooting angles, the spatial geometric information at least comprising: at least one target spatial point and at least one spatial line segment, M being an integer greater than 1; and according to the spatial geometric information, a three-dimensional Gaussian ellipsoid set is generated, and the three-dimensional Gaussian ellipsoid set comprises at least one three-dimensional Gaussian ellipsoid used for representing three-dimensional scene information of the shooting scene.

The invention discloses an ancient fresco restoration method based on progressive reconstruction and damage perception self-adaption, and relates to the technical field of computer vision and image processing, a damaged fresco image is input into a coarse restoration network for initial restoration, in the initial restoration process, a discriminator and the coarse restoration network are adopted for adversarial training, and a damaged fresco image is obtained; generating an initial restoration result of the mural image; inputting the initial repair result and the mask image into a mask-guided local information extraction network to obtain a local optimization result; and inputting the local optimization result into a global information extraction network to obtain an overall restoration result of the mural image. The problem that an existing network is low in efficiency in the aspect of capturing local details and global styles is solved. The method has good applicability to damaged wall paintings; the problem of fuzzy texture in the repairing result is effectively solved; local features are adaptively extracted and fused according to the damage degree of the mural, and the multi-stage residual information distillation module further refines details of the mural on different scales.

The invention discloses a hyperspectral image classification method and system based on a double-branch lightweight algorithm, belongs to the technical field of computer vision and image processing, and solves the problems that an existing image classification model is complex in structure, so that the calculated amount is large, the occupied memory is high, and balance between classification precision and model lightweight is difficult to achieve. The method comprises the steps of collecting a hyperspectral image, preprocessing the hyperspectral image, performing iterative training on a double-branch lightweight algorithm model based on a training set, executing the double-branch lightweight algorithm model, and classifying a test set by the double-branch lightweight algorithm model. According to the hyperspectral image classification method, the algorithm complexity is greatly reduced, the high precision of hyperspectral image classification is ensured, the lightweight space-spectrum Transformer is introduced into the double-branch lightweight algorithm model, and through the optimization design of the whole structure and the lightweight efficient Attention-Aware mechanism, the classification accuracy of the hyperspectral image is improved. The complexity of the model is reduced, and the characteristic relation between the modeling space and the spectrum sequence can be better established.

The invention relates to the technical field of computer vision and image processing, and provides a depth synergy graph Hash image retrieval method based on attention enhancement, which comprises the following steps: acquiring a to-be-retrieved image; extracting image features of the to-be-retrieved picture; performing weighting processing on the image features by using an attention mechanism to obtain weighted image features; encoding the weighted image features into a hash code; hash codes of pre-stored images in an image database are obtained, Hamming distances between the Hash codes of the to-be-retrieved images and the Hash codes of the pre-stored images are calculated, the pre-stored images are sorted according to the sequence of the Hamming distances between the Hash codes of the to-be-retrieved images and the Hash codes of the pre-stored images from small to large, and the first multiple pre-stored images are output to serve as similar images of the to-be-retrieved images; the Hash codes obtained through the image retrieval method are high in discrimination capability and high in image retrieval precision.

The invention discloses a super-fractal multi-modal depth fusion-based virtual viewpoint occlusion area restoration method, which belongs to the technical field of computer vision and image processing, and comprises the following steps of: performing multi-modal feature extraction on an input image by utilizing a constructed super-fractal multi-level feature fusion network; performing depth information estimation on the periphery of the occlusion region of the input image by using the constructed depth estimation model to obtain occlusion region filling content; selecting a current restoration scheme from given restoration schemes based on the obtained multi-modal features of the filling content of the occlusion area, and performing preliminary restoration on the occlusion area of the input image according to the weight of each modal feature in the current restoration scheme; and evaluating the preliminary restoration effect, and continuously optimizing the weight of each modal feature of the current restoration scheme when the restoration effect does not conform to the expectation until an image of which the restoration effect of the occlusion region meets the expectation is output. According to the invention, the quality and visual effect of the virtual viewpoint can be improved.

The invention relates to the technical field of computer vision and image processing, and discloses a low-light image enhancement method based on a conditional diffusion model and an attention mechanism. The method comprises the following steps: converting an input low-light image and a normal-light image from an RGB color space into an HVI color space, and decomposing the HVI color space; obtaining a brightness component and a horizontal and vertical component; performing iterative brightness recovery on the obtained brightness component through a conditional diffusion model to obtain an enhanced brightness component; performing color preservation and local and global detail enhancement on the obtained horizontal and vertical components through a residual attention module to obtain enhanced horizontal and vertical components; and synthesizing the obtained enhanced components into an HVI image, converting the HVI image back to an RGB image, and finally obtaining an enhanced image. According to the method, the problem that an enhancement algorithm in the prior art is not easy to obtain a local detail which is fully reserved, the color is kept undistorted, and the generalization ability is improved to adapt to images under different conditions is solved.

The invention discloses an underwater image enhancement method based on relation-driven state space modeling, belongs to the technical field of computer vision and image processing, and aims to solve the problems of color distortion, detail blurring and the like of an underwater image caused by water attenuation and scattering. Carrying out structure perception enhancement modeling; and image reconstruction and decoding output. Wherein the structure sensing module extracts spatial continuity information through an offset generation network, adaptively rearranges scanning paths, preferentially focuses on a semantic rich region and executes dynamic state propagation, so that the accuracy and interpretability of global modeling are improved; and meanwhile, a local convolution kernel is dynamically generated according to global channel statistical characteristics by inputting a dependent convolution branch, so that the adaptability to a background region is enhanced. In order to further improve the feature fusion effect, a cross-feature fusion bridge module is provided, multi-level features are guided and fused through bidirectional attention of a structural path and a semantic path, and detail information and context semantics are effectively integrated.

The invention relates to the technical field of image recognition, in particular to a quality detection and recognition method. The method comprises the steps of performing spatial mapping on an original image, performing feature extraction on the mapped image, performing step-by-step segmentation on an obtained image to be layered, extracting a required layer, forming color features by using color information of the layer, performing regression judgment through machine learning training, and obtaining a score. The color features are calculated through distribution of hue, saturation and brightness of each pixel in the image and comprise one or more combinations of a mean value, a standard deviation and a deviation. Through computer vision and image processing technologies, an optimal detection area in a sample is automatically identified and analyzed, stratified analysis is carried out, multi-dimensional quality data of the sample is detected, and rapid and accurate acquisition and quality evaluation of the multi-dimensional quality data of the sample are realized. For the evaluation standard difference of different groups, only the evaluation score Y value needs to be adjusted, the model does not need to be trained again, and the application range of the method is greatly widened.

The invention discloses an RGBT target tracking network and method fused with a multi-interaction feature enhancement mechanism, relates to the technical field of computer vision and image processing, and aims to solve the problems that multi-modal fusion is insufficient and tracking is easy to drift due to fixed or blind updating of a template. The network adopts an end-to-end tracking framework, a backbone network of the network extracts visible light and infrared template images and searches feature tokens of the images through a convolution token embedding module, and performs intra-modal and inter-modal mixed attention interaction by using a multi-interaction Transform module to realize multi-level feature fusion. A target frame is output by adopting an angular point prediction head, a template updating module is introduced, and a template token is dynamically evaluated and updated through two Transform modules, so that the long-term tracking stability is improved. The network effectively deals with complex environment changes by fusing multi-modal information and a dynamic updating mechanism, and the tracking accuracy and robustness are remarkably improved.

The invention relates to an X corner detection method based on multilevel matching and feature optimization, and belongs to the technical field of computer vision and image processing. The method comprises the following steps: making a high-precision template corresponding to a predetermined X corner mark, so that the high-precision template can be accurately focused on a corner area; carrying out noise reduction and filtering operations on the image before identifying the mark so as to remove noise interference; carrying out template matching on the preprocessed image by adopting a self-adaptive threshold value and a multi-scale search strategy; and performing multi-level detection on the basis of template matching, including ORB feature extraction, Harris corner optimization and sub-pixel level positioning, and outputting the accurate position of the corner and a feature descriptor. The method can still maintain high precision and stability in a complex scene, is suitable for the fields of visual positioning, three-dimensional reconstruction and the like, and has the advantages of high detection efficiency, strong anti-interference capability, wide applicability and the like.

The invention discloses an intelligent identification system for puffed corn, and relates to the technical field of computer vision and image processing, and the system comprises an image collection module which integrates multispectral imaging and a near-infrared sensor and is used for obtaining surface morphology, internal structure and moisture content data of puffed corn; an environment light intensity sensor and an LED array are arranged in the self-adaptive optical compensation module, the wavelength and illumination intensity of a light source are adjusted in a closed-loop feedback mode, and color feature deviation caused by workshop environment light changes is compensated; the image processing module is used for deploying a lightweight hybrid neural network model and is used for adhesive particle segmentation and defect detection; the real-time sorting control module is used for driving a three-axis mechanical arm and a pneumatic spray valve according to an identification result so as to realize defective product rejection and grade subpackaging; and the digital twinning optimization platform is used for outputting a process adjustment suggestion to the production line PLC. According to the invention, through the holographic sensing network, the hybrid intelligent decision and the cross-domain cooperative control, the problems of inaccurate reading, unquick judgment and inaccurate control in puffed corn identification are solved.

The invention discloses a method and device for automatically segmenting a material and refining an animation local target, and belongs to the technical field of combination of the computer vision field and the image processing image, and the method comprises the steps: constructing a material target detection model based on a target detector Grouping DINO, and carrying out the target detection of an animation image based on an input material classification text; performing material target segmentation on the animation image by adopting a target segmentation model and taking a material target frame obtained by target detection as guide information to obtain a mask pattern of a material target, and obtaining an animation local pattern corresponding to the material target from the animation image based on the mask pattern; an image refining model is constructed based on a generative adversarial network, refining processing is performed on an animation local graph, and a refined local material image is attached to an animation image, so that a local material target is efficiently and accurately segmented and extracted from the animation image under a complex background, and further refining processing is performed on the local material target. The detail display effect is improved.

The invention discloses a vegetation image recognition method, system, equipment and medium based on OfficientNet, and belongs to the technical field of computer vision and image processing, and the method comprises the steps: collecting data through image collection and synchronous utilization of a laser radar, and carrying out the preprocessing of the collected data; an improved OfficientNet network model is constructed, and training and optimization are carried out by using the improved OfficientNet network model; and carrying out vegetation classification and hidden danger detection on the input image, inputting the collected real-time data into a comprehensive risk scoring model for hidden danger detection for calculation, and carrying out risk early warning positioning. The hidden danger trees are accurately positioned based on the GIS technology, the omission ratio is reduced through a dynamic early warning system, and the manual inspection risk is reduced through automatic reporting; different climate monitoring requirements are adapted based on the expansibility of the model, the tree barrier hidden danger can be blocked and the maintenance efficiency can be improved in practical application, and an integrated solution integrating accurate identification and real-time positioning is formed.

The present invention discloses a method and system for industrial defect detection and localization based on frequency domain enhancement, which relates to the fields of computer vision and image processing, can discover abnormal patterns in industrial images from multiple perspectives, and also has a relatively fast inference speed. The method includes: processing an input image through a high-pass filter to obtain a high-frequency image; inputting the original image and the high-frequency image into an improved U-Net network integrated with a dual-domain feature selection module; performing image reconstruction simultaneously in the spatial domain and the frequency domain; calculating an anomaly score and a localization map based on the reconstruction results. By combining spatial domain and frequency domain analysis, the present invention realizes a comprehensive characterization of local pixel changes and global frequency patterns in the image, improving the accuracy of anomaly detection and localization. The method has achieved optimal performance on multiple real industrial scenario datasets. The present invention is applicable to anomaly detection and anomaly area localization of industrial product images.

The invention discloses an industrial defect detection and positioning method and system based on frequency domain enhancement, relates to the field of computer vision and image processing, can discover abnormal modes in an industrial image from a plurality of perspectives, and also has a relatively high reasoning speed. The method comprises the following steps: processing an input image through a high-pass filter to obtain a high-frequency image; inputting the original image and the high-frequency image into an improved U-Net network integrated with a double-domain feature selection module; image reconstruction is carried out in a spatial domain and a frequency domain at the same time; an anomaly score and a localization map are calculated based on the reconstruction result. According to the invention, by combining spatial domain and frequency domain analysis, comprehensive characterization of local pixel change and global frequency mode of the image is realized, and the accuracy of anomaly detection and positioning is improved. The method achieves the optimal performance on a plurality of real industrial scene data sets. The method is suitable for abnormal detection and abnormal area positioning of industrial product images.