669 results about "Edge enhancement" patented technology

The invention discloses a solar silicon wafer visual detection method, and particularly relates to the technical field of silicon wafer detection. The method comprises the following steps: acquiring optical images of different incident angles, synchronously acquiring infrared transmission images, performing normalization processing, edge enhancement filtering and self-adaptive threshold segmentation on the acquired multi-angle images, extracting suspected crack areas, and respectively calculating a gray gradient change rate and a stress abnormal degree, so as to obtain a suspected crack area; calculating a crack credibility score according to the gray gradient change rate and the stress abnormal degree through a weighted scoring method, and comparing the crack credibility score with a preset crack detection threshold value to distinguish real hidden cracks and stress pseudo defects; if the credibility score is lower than the threshold value, secondary sampling is carried out by adjusting the illumination angle, and the judgment result is further optimized; according to the method, the false rejection rate caused by lattice stress pseudo defect misjudgment is effectively reduced, meanwhile, missing detection of real subfissures is avoided, and the accuracy, stability and production yield of silicon wafer defect detection are improved, so that the long-term reliability of a photovoltaic module is enhanced.

The invention discloses an MRI medical image segmentation method and system based on deep learning, and the method comprises the steps: obtaining a mammary gland MRI image, carrying out the standardization processing of the mammary gland MRI image, and removing a non-region of interest to obtain a target MRI image; performing detail enhancement on the suspected lesion area to reconstruct an image to be preprocessed; performing noise reduction processing, contrast enhancement and edge enhancement preprocessing on the to-be-preprocessed image to generate three versions of input images, and inputting the three versions of input images into a pre-trained deep learning model as three channels to complete breast tumor identification; and carrying out image segmentation on the breast tumor region, and classifying segmented breast tumors. According to the MRI medical image segmentation method and system based on deep learning provided by the invention, accurate and efficient processing of the mammary gland MRI image is realized, efficient identification, accurate segmentation and accurate classification of the mammary gland tumor are realized, the accuracy and robustness of mammary gland tumor detection can be improved, and efficient and reliable auxiliary diagnosis is provided for clinic.

The invention relates to the technical field of image recognition, in particular to a circuit board micropore copper plating quality inspection method based on image recognition. The method comprises the following steps: setting an annular polarized light source array; performing dynamic depth-of-field fusion shooting on the circuit board micropore based on the annular polarized light source array to obtain a two-dimensional projection drawing of the circuit board micropore; performing sub-pixel edge enhancement on the circuit board micropore two-dimensional projection drawing to generate a circuit board micropore enhanced projection drawing; and segmenting the interest boundary of the circuit board micropore enhanced projection drawing to obtain the hole wall region-of-interest boundary. Advanced image recognition, deformation analysis and self-learning technologies are introduced, efficient, accurate and automatic circuit board micropore copper plating quality intelligent inspection is achieved, and the defects of a traditional method in the aspects of detection precision, efficiency and comprehensive evaluation capacity are overcome.

The invention relates to the technical field of gesture recognition, in particular to an anti-interference optimized gesture recognition method. Comprising the following steps: acquiring a gesture video stream through a camera, constructing a dynamic background model by using a frame difference method and a Gaussian mixture model, eliminating a static background and interference, and extracting a target area image; performing local brightness histogram analysis on the target region image, and optimizing the image quality by adopting a region adaptive compensation algorithm and a multi-scale edge enhancement technology; positioning a gesture area in real time by using a color histogram and a feature matching algorithm, and dynamically updating a gesture track in combination with Kalman filtering; and extracting gesture shapes, tracks and dynamic mode features through deep learning, comparing the features with a standard model library, and outputting gesture categories and corresponding function instructions. According to the method, a multi-level optimization strategy is adopted for a complex background, a dynamic target and a changeable illumination environment, so that the anti-interference capability and the recognition precision of gesture recognition are improved.

The invention relates to the technical field of photoetching machine calibration, and discloses a photoetching machine calibration method, device and equipment based on multi-view vision, and the method comprises the following steps: carrying out imaging and phase sensitive detection analysis on a mask plane and a wafer plane through a four-path optical beam splitting system to obtain four groups of mask-wafer initial alignment position information; performing scanning white light interference edge enhancement processing to obtain three-dimensional surface contour data; performing wavelet transform processing on the three-dimensional surface contour data, and performing dynamic registration on the four-path optical beam splitting system to obtain multi-view visual feature registration data; a comprehensive error model including mechanical errors, optical errors and environmental errors is established, real-time correction and error compensation are carried out on a six-degree-of-freedom motion platform of the photoetching machine, a multi-view vision calibration result of the photoetching machine is obtained, the influence of environmental vibration and thermal drift on calibration precision is effectively eliminated, and the calibration accuracy of the photoetching machine is improved. The optical system is ensured to be always in the optimal imaging state, and the calibration precision is improved.

The invention provides a liver focus three-dimensional modeling method, and belongs to the technical field of image processing based on computer vision. Firstly, a multi-view spatial registration method based on optical flow optimization is designed, pixel-level displacement information of different view images is estimated by calculating an optical flow field, accurate image alignment is achieved, and spatial consistency of three-dimensional reconstruction is improved. And secondly, a three-dimensional reconstruction strategy based on two-dimensional focus segmentation is proposed, the two-dimensional focus segmentation is completed by adopting a lightweight U-Net variant, and a segmentation result is mapped to a three-dimensional space through a voxel probability projection method, so that 3D focus reconstruction is realized, and the calculation cost is reduced. And finally, extracting high-frequency features of the three-dimensional model by adopting a local edge enhancement method based on a Laplacian operator, and strengthening a focus boundary and a key anatomical structure through interpolation optimization, so that the three-dimensional model is more accurate and clearer. Compared with a traditional method, the method has the advantages that the mode of purely depending on image superposition is avoided, and the accuracy of three-dimensional modeling is improved.

The invention relates to the technical field of image processing, and provides an enteromorpha remote sensing image detection method and system based on deep learning, and the method comprises the following steps: carrying out the edge gradient extraction and small target enhancement processing of an obtained to-be-detected remote sensing image, and obtaining a first feature map after the edge enhancement and small target feature enhancement; transmitting the first feature map to a U-shaped backbone network formed by cascading multiple stages of Ep-VSS block modules, performing multi-stage feature extraction, and obtaining a detection result of the enteromorpha remote sensing image based on the feature map output by the last stage of Ep-VSS block module; according to the method, through edge enhancement, small target sensitive detection, multi-scale texture extraction and spatial context modeling, precise boundary segmentation and long-range dependence modeling are realized, and the accuracy, real-time performance and reliability of enteromorpha remote sensing monitoring are improved.

The invention relates to an image processing method for an underwater robot heterogeneous imager array, and the method comprises the steps: S1, collecting an original underwater image, and carrying out the brightness adjustment, and obtaining a first image with balanced brightness; s2, analyzing pixel distribution of the first image, and judging and executing de-scattering processing to obtain a second image; s3, performing multi-scale detail enhancement on the second image to obtain a third image with richer details; s4, evaluating color distribution of the third image, and performing color correction to obtain a fourth image; s5, performing adaptive denoising processing on the noise features of the fourth image to obtain a fifth image; s6, according to the contrast characteristic of the fifth image, contrast adjustment is carried out to ensure that a preset threshold value is met, and a sixth image is obtained; s7, edge enhancement processing is carried out on the sixth image, and a seventh image with the clear edge is obtained.By means of illumination compensation, scattering removal and multi-scale detail enhancement, the influence of complex optical conditions of the underwater environment on the image quality is effectively overcome.

The invention discloses a remote sensing image fishpond extraction method based on an adaptive edge enhanced neural network. The method comprises the following steps: selecting a sentinel No.2 satellite multispectral band to synthesize a false color image; constructing a fishpond region labeling data set; establishing an adaptive edge enhanced neural network architecture, wherein the architecture fuses an edge detection module capable of learning a threshold and semantic feature extraction; an edge perception double attention module and a pyramid pooling module are integrated, and the multi-scale feature representation capability is enhanced; optimizing a training process by adopting a multi-level depth supervision and prediction fusion strategy; and finally, extracting a fishpond area through the training model and generating a standardized result. According to the method, the fishpond boundary segmentation precision is remarkably improved, the adaptability to fishponds with different scales is enhanced, the detection rate of small-scale fishponds is particularly improved, the model convergence process is accelerated through the depth supervision strategy, misclassification of similar water bodies such as rivers and ditches is effectively reduced, and the accuracy of fishpond boundary segmentation is improved. The technical problems that in the prior art, edges are fuzzy, multi-scale adaptability is poor, complex background interference is sensitive, and the omission ratio of a small-scale fishpond is high are solved.

The invention provides an image recognition and processing system based on deep learning. The image recognition and processing system comprises an image acquisition module for acquiring original image data; the image preprocessing module performs noise suppression, edge enhancement and color correction on the image; the multi-scale feature extraction module extracts multi-dimensional features through a cascade convolutional layer and a self-attention mechanism; the self-adaptive classification and identification module performs multi-class classification based on a dynamic weight distribution algorithm; the dynamic optimization control module adjusts hyper-parameters in real time through a multi-objective optimization algorithm; the result output module outputs a classification result and a confidence score; and the feedback learning module updates the network weight according to the confidence score. All the modules work cooperatively, and efficient and dynamic image recognition and processing are achieved. According to the method, efficient and accurate image recognition and processing can be realized, and the adaptability, classification accuracy and robustness of the system are improved.

The invention provides a rock image classification method based on edge enhancement and multi-scale feature fusion. By combining the edge enhancement and multi-scale feature fusion technology, the accuracy of mineral classification in the rock slice image is remarkably improved. According to the method, rock slice image interference is eliminated through filtering and denoising, and mineral particle edges are enhanced by fusing morphological top-hat transformation and a Laplace operator; and performing multi-scale pyramid decomposition on the enhanced image to extract high-frequency information, performing multi-direction response enhancement to generate a direction feature map, and performing channel-level fusion on the original image, the edge enhanced image and the direction feature map. And finally, inputting the fused image into a convolutional neural network to complete rock type classification. According to the method, mineral boundary expression is effectively enhanced, the classification accuracy is improved, and a reliable technical scheme is provided for geological analysis and lithology identification.

The invention discloses a medical image fuzzy boundary segmentation system and method based on COD frequency domain guidance. In order to solve the problem of boundary fuzzy segmentation of a medical image under a complex background, frequency domain feature processing and an improved camouflage target detection (COD) algorithm are innovatively combined, and a progressive boundary optimization segmentation framework FEA-Net is designed. According to the framework, four key modules are designed: a semantic edge enhancement module (SEEM) realizes boundary preliminary positioning through space-channel self-attention; on the basis of SEEM, a frequency domain feature guide enhancement module (FTEM) further improves boundary prediction precision by using Fourier transform and adaptive frequency selection; an adaptive edge feature module (AEFM) highlights key boundary features; a Context Attention Aggregation Module (CAAM) implements feature fusion. Experiments show that evaluation indexes of the method on data sets of Kvasair-SEG, ISIC2018 and KPIs2024 are remarkably improved, particularly, obvious advantages are shown when boundary fuzzy areas are processed, and reliable support is provided for accurate segmentation of medical images.

The invention provides a fuzzy barcode image processing method and system fused with super-resolution repair. The method comprises the following steps: acquiring a bar code image containing dynamic blurring, detecting a blurred region formed by superposition of motion tracks in the bar code image based on space-time continuity characteristics of the dynamic blurring, extracting an edge diffusion direction of the blurred region, and determining the dynamic blurring of the bar code image according to the edge diffusion direction. And performing multi-scale detail layer generation on the fuzzy region, inputting the multi-scale detail layer into a fusion repair module, and generating a repaired high-resolution barcode image by alternately executing super-resolution reconstruction guided by local high-frequency information and morphological repair of a module gap. Edge sharpening and noise suppression are carried out on the high-resolution barcode image, and a clear image meeting the barcode decoding standard is output; according to the technical scheme provided by the invention, the restoration precision and reliability of the complex dynamic fuzzy scene are remarkably improved, and the output image strictly meets the geometric constraint requirement of bar code decoding.

The invention discloses an efficient target detection method in a foggy environment, and relates to the technical field of target detection. According to the method, a lightweight foggy day target detection model is established and called DF-DETR, a defogging module of a double-branch structure is designed through an edge enhancement module, and target features of a foggy day image are effectively captured; then, a dual convolution feature extraction module DualConv-Block is designed, so that feature extraction is enhanced, and meanwhile, the complexity and the calculation amount of the model are remarkably reduced; besides, an EAA attention mechanism is combined with an intra-scale feature interaction module to form an AIFI-EAA module, and the AIFI-EAA module is integrated into the hybrid encoder, so that the attention capability of the model on dense targets is improved, and missing detection and false detection are effectively reduced; finally, a dynamic sampling scale attention feature fusion module is designed, alignment of multi-scale features is achieved through dynamic up-sampling, the flexibility and robustness of feature expression are enhanced, and the fusion and expression ability of the multi-scale features is further optimized.

The invention relates to the technical field of image enhancement, and discloses an image enhancement method based on an ISP image signal processing visual sensor, and the method comprises the steps: carrying out the collection of multiple frames of images of the same scene, carrying out the analog-to-digital conversion of the collected multiple frames of images, generating an RGB three-channel digital matrix, and obtaining N frames of original image sequences; dark current correction and lens shadow correction are executed, an affine transformation matrix from the N-1 frame to the first frame of image is calculated based on SIFT feature point extraction and matching, and an image sequence after space alignment is obtained; performing spatial feature extraction to obtain a multi-scale feature map sequence, and performing residual feature connection operation to obtain a time sequence correlation feature map sequence; according to the method, attention calculation of the channel dimension and the space dimension is executed, meanwhile, structural layer edge enhancement and texture layer denoising processing are executed, a target enhanced image is obtained, various kinds of distortion and noise interference in the image acquisition process are reduced, and the definition and the detail fidelity of the enhanced image are ensured.

The invention discloses an industrial image change anomaly detection method and system based on artificial intelligence, and relates to the technical field of image recognition, and the method comprises the steps: collecting a dual-light-source industrial image, employing frequency domain saliency to guide fusion, and carrying out visual enhancement processing through color mapping and edge enhancement; inputting the enhanced image into a CNN convolutional network to generate a multi-scale feature map, extracting a multi-scale high-pass residual image through two-dimensional fast Fourier transform and a high-pass filtering template, and splicing all scales and coding to generate a token sequence through local attention guide fusion; constructing a self-induction visual model, and performing feature reconstruction on the token sequence to generate a reconstructed feature map; and calculating and reconstructing an error scoring graph by adopting double error indexes, sampling to obtain an abnormal smooth graph, and segmenting an abnormal region based on the abnormal smooth graph. And finally, a dual anomaly detection system of image-level judgment and region-level identification is constructed.

The invention discloses a carton printing defect detection method and system based on visual identification, and relates to the field of defect detection, and the method comprises the steps: obtaining a printing carton image set; constructing an edge enhancement detection model based on YOLov; taking the image set as input, and extracting a plurality of feature maps with different resolutions by using a backbone network; inputting a plurality of feature maps with different resolutions into a neck network, carrying out adaptive aggregation through a feature pyramid module to obtain an aggregated feature map, then obtaining a direction and an edge response weight through an edge sensing module, and carrying out edge reservation fusion to obtain an edge enhancement feature; and inputting the edge enhancement features into a detection network, and obtaining defect detection positions and types through multi-task training of position perception loss and direction enhancement classification loss. Aiming at the low detection precision of the printing defects of the carton, the detection precision of the ink dot and broken line defects is improved through an edge reservation fusion strategy, adaptive feature fusion and the like.

The invention relates to a remote sensing image change detection method of a difference enhancement network based on edge guidance, and the constructed difference enhancement change detection network mainly comprises a backbone network, an edge enhancement module, a double-branch difference enhancement module, a multi-scale difference feature fusion module and a decoder. Edge information of the changed object is refined through an edge enhancement module, so that feature information with clear boundaries can be extracted for the changed object; a dual-branch difference enhancement module effectively learns the difference between the foreground and the background. The module captures global and local information of a dual-time image through space and channel branches, emphasizes a change area, inhibits noise interference, and enhances long-range dependence of difference. According to the method, the detection performance of the small object under the high-resolution remote sensing image is improved by using a multi-scale difference feature fusion method. Experimental results show that the detection effects of the proposed network on a plurality of public data sets are superior to those of several common remote sensing image change detection methods at present.

The invention relates to the cross technical field of computer vision and civil engineering, and discloses a deep learning-based bridge disease image segmentation method, which comprises the following steps of: establishing an image data set containing crack and spalling diseases and performing online enhancement; constructing a segmentation network model comprising a frequency dynamic convolution encoder branch, an edge enhancement Transform encoder branch, a gating cooperation unit, a decoder and a depth supervision module; training the model by using a weighted mixed loss function; and inputting the test set to obtain a final segmentation mask. Self-adaptive fusion of local texture features and global context information is realized through a dual-encoder architecture and a gating cooperation mechanism; a frequency dynamic convolution and edge enhancement module is utilized to enhance the anti-noise capability and micro-disease perception under a complex background; and in combination with a category weighting strategy, the problem of pixel category imbalance is effectively solved, and high-precision automatic segmentation of concrete bridge diseases is realized.

The invention discloses a seal removing and document repairing method based on quantum state collaborative regulation and control, and relates to the field of document image processing and quantum computing cross technology, the method comprises the following steps: obtaining to-be-processed information, and carrying out quantum-classical feature collaborative preparation; the character stroke continuity is guaranteed through quantum entangled state modeling and entanglement degree constraint iteration, texture decoupling is achieved through quantum wavelet transform and Gram-Schmidt orthogonalization, and adaptive filling is conducted in combination with a quantum generative adversarial network; dynamic quantum phase adjustment is used for counteracting superposition interference of stamps with different transparency, and quantum neural network noise reduction and multi-scale quantum Fourier sharpening are used for optimizing image quality; checking the repair result, if the repair result does not reach the standard, returning to the edge sharpening link to perform decoupling and filling the edge sharpening link to readjust the parameter; and for special scenes such as inclination, multi-color overprinting and ultra-thin frames, quantum rotation correction, color channel separation and boundary annihilation operator processing are used, finally, high-precision, high-naturalness and high-adaptability restoration of seal removal is achieved, and high fidelity of results is guaranteed.

The invention provides a high-reflection plane metal part defect detection method and system based on a composite polarized light source, and a medium. The method comprises the following steps: illuminating a high-reflection plane metal part based on the composite polarized light source, adjusting a light source polarization angle and an LED wave band combination to construct a shooting environment, and collecting an image sequence; calculating a polarization intensity distribution parameter for the image sequence based on the relationship between the polarization intensity and the Stokes parameter to obtain a polarization intensity distribution image; analyzing the polarization intensity distribution image based on a polarized light reflection model, and outputting a specular reflection component and a diffuse reflection component; fitting the specular reflection component and separating the diffuse reflection component to obtain a reflection-removed image; based on an image enhancement algorithm, edge enhancement is carried out on the reflection-removed image to obtain an enhanced image, and based on a defect identification algorithm, the enhanced image is analyzed to obtain defect information; by removing the diffuse reflection component and reconstructing the image, the mirror interference is effectively stripped, and the defect identification precision is improved.

The invention belongs to the technical field of image processing, relates to a multi-scale road crack detection method and device in extreme weather and a medium, and relates to the technical field of image processing, and the method comprises the steps: constructing an extreme weather data set; constructing a target detection network; training a target detection network by adopting the data set to obtain a target recognition model; according to the method, the lightweight backbone network CAPNet is adopted to replace a backbone network of the RT-DETR, and the feature extraction efficiency is improved; a cascade multi-branch feature fusion module is used for replacing a CCFM module in a traditional neck network, dynamic integration of multi-scale features is achieved, a multi-scale edge enhancement module is additionally arranged in a network structure, the perception ability of the model to crack edges of different scales is enhanced, the feature expression problem under the low contrast and blurred image conditions is effectively solved, and the dynamic integration of the multi-scale features is achieved. And the balance between the detection precision and the efficiency is realized.

The invention discloses a photovoltaic module infrared image fault detection method based on unmanned aerial vehicle inspection, which relates to the field of fault detection, and comprises the following steps: configuring an unmanned aerial vehicle platform, planning a flight path, setting aerial photography parameters, setting an infrared thermal image acquisition and infrared image data return and storage mechanism, and performing gray normalization processing, image noise reduction, histogram equalization, edge enhancement processing and size standardization processing on the infrared image data, and performing data enhancement operation. An unmanned aerial vehicle infrared inspection technology is combined with a deep learning target detection model, a set of complete photovoltaic module infrared image fault detection process is established, and full-process automatic processing from image acquisition, image preprocessing, model detection to result evaluation and visualization can be realized. The method has the comprehensive advantages of being high in fault recognition precision, high in detection speed, standardized in processing flow and the like, and the efficiency and the intelligent level of photovoltaic power station component-level fault inspection are remarkably improved.

The invention relates to the technical field of medical image processing, in particular to a cross-modal image fusion detection system for endoscopic early cancer lesions, which comprises a data acquisition module for acquiring a white light endoscope and a narrow-band imaging image; the data annotation module carries out pixel-level annotation, and the data enhancement module amplifies a data set by using CycleGAN; the white light data module and the narrowband data module train feature extraction models respectively; the feature alignment and attention module solves the problem that feature scales and semantics between two modes are inconsistent, and efficient fusion is achieved. The edge enhancement module enhances feature extraction of small focuses through the generative adversarial network; the fusion model construction module fuses the features and the transition area data to generate a fusion model; and the output module processes the white light and the narrow-band imaging image and outputs a fused image. Through feature alignment, an attention mechanism and a CyclGAN data enhancement technology, the problem of insufficient medical image data is effectively solved, the model generalization ability is enhanced, and efficient and accurate early cancer focus detection is realized.

A scene text detection method, comprising: acquiring an image to be subjected to detection, and performing feature extraction on said image, wherein said image contains scene text; processing features obtained by means of the feature extraction; and on the basis of a processing result, performing core-region probability prediction and boundary threshold prediction on the scene text, so as to obtain a binary image of said image, wherein the processing at least comprises one of the following: attention feature fusion and first fusion processing, and joint attention processing, or the processing at least comprises one of the following: feature screening and second fusion processing, and segmentation processing, edge enhancement and third fusion processing, and the feature extraction comprises: performing feature extraction on said image by means of a convolutional neural network, and adjusting a feature dimension of a plurality of extracted branches to a preset dimension, so as to obtain a shallow feature of the plurality of branches.

The invention discloses a multi-scale SAR (Synthetic Aperture Radar) image ship detection method and system based on edge enhancement and diffusion denoising, and mainly solves the problems that the existing SAR ship detection method is sensitive to noise and poor in small target feature extraction capability. According to the implementation scheme, the method comprises the following steps: obtaining an SAR image, carrying out standardized preprocessing, inputting the SAR image into a deep convolutional neural network, extracting a multi-scale feature map, and carrying out dynamic channel fusion enhancement on a low-layer feature map in the multi-scale feature map to obtain a fused high-quality feature map; performing differential edge enhancement on middle and high-level feature maps in the multi-scale feature map, and inputting the enhanced feature map and the fused feature map into a diffusion model detection head for training; and inputting a to-be-detected SAR image into the trained diffusion model detection head, outputting a preliminary target bounding box and a category confidence coefficient, performing score screening and non-maximum suppression operation on the preliminary target bounding box and the category confidence coefficient, and generating a final ship target detection result. According to the method, the precision and robustness of SAR image ship detection are remarkably improved, and the method can be used for ocean monitoring and military reconnaissance.

The invention discloses a design method of a monolithic polarization multiplexing dual-function super-structure lens device and the super-structure lens device, and belongs to the technical field of optical imaging. The designed device can respectively realize independent bright field and edge enhancement imaging functions according to the polarization state of incident light; the cross section of the nano unit forming the device is in an anisotropic C2 symmetrical shape, and the long axis and the short axis of the cross section are perpendicular to each other, so that linearly polarized light parallel to and perpendicular to the long axis in the polarization direction has independent phase delay after passing through the nano unit. A bright field and an edge enhancement imaging phase are respectively applied to a pair of orthogonal polarization states, so that the device can carry out bright field imaging in one polarization state and carry out edge enhancement imaging in the other polarization state, and the imaging efficiency can be improved under the conditions that other imaging devices are not needed and the resolution requirement is met. And dual-function imaging with relatively wide working bandwidth and relatively high imaging mode switching convenience is realized.

The invention discloses a multi-scale frequency-space fusion camouflage target detection method and system, and belongs to the technical field of camouflage target detection, an image and wavelet transform of the image are input into a WaveCamoNet model for camouflage target detection, and the model comprises a double-flow feature extraction module, a cross-domain dynamic fusion module, an edge texture enhancement module and a hierarchical decoder; inputting the image and the wavelet transform of the image into a double-flow feature extraction module, and extracting multi-scale spatial domain features and frequency domain features; inputting the multi-scale spatial domain features and the frequency domain features into a cross-domain dynamic fusion module, and performing multi-scale fusion and cross-domain dynamic fusion to obtain multi-scale fusion features and cross-domain dynamic fusion features; inputting the cross-domain dynamic fusion features into an edge texture enhancement module, and performing spatial calibration to obtain edge enhancement features; and inputting the multi-scale fusion features, the cross-domain dynamic fusion features and the edge enhancement features into a hierarchical decoder for decoding to obtain a binary detection image, and completing pixel-level positioning of the camouflage target.

The invention provides a real-time monitoring and analyzing system for tooth orthodontic correction, and relates to the technical field of oral treatment, the real-time monitoring and analyzing system comprises an information capturing system, an intelligent operation system and an appliance dynamic regulation and control system, the information capturing system comprises a three-dimensional oral cavity imager, a health condition evaluator, a multifunctional induction network and a portable monitoring device, the three-dimensional oral cavity imager generates a three-dimensional oral cavity image through triangulation positioning, extracts oral cavity mucous membrane features by combining an improved local binary pattern algorithm and adding an edge enhancement filter, and confirms the identity when the Euclidean distance is calculated to be smaller than a preset threshold value. According to the system, mucous membrane texture features are extracted through combination of the three-dimensional oral cavity imager and the improved LBP algorithm for identity verification, the health condition assessment instrument collects temperature, humidity and pH value every 6 minutes, the portable monitoring device collects pressure and biological signal data, and comprehensive collection of multi-dimensional data is achieved.

The invention relates to the technical field of dough kneading control, in particular to an intelligent dough kneading mode selection method and system. The method comprises the following steps: acquiring a real-time dough monitoring image in a dough kneading machine; edge sharpening reconstruction is carried out on the real-time dough monitoring image, sequential frame sequence fitting is carried out, and a dough monitoring sequential frame sequence is constructed; performing frame-by-frame dough contour recognition on the dough monitoring time sequence frame sequence, performing dough time sequence state deep mining, and constructing a dough time sequence state diagram; performing frame-by-frame yeast particle dynamic change mining and multi-moment activity trend prediction on the dough monitoring time sequence frame sequence so as to extract an optimal dough kneading time point; and performing time state matching on the dough time sequence state diagram based on the optimal dough kneading time point, and performing dynamic dough kneading parameter decision to generate dynamic dough kneading parameters. Fine control over the dough kneading machine is achieved, various production environments are automatically adapted, and the intelligent degree of the dough kneading process and the quality of dough are improved.