2264 results about "Spectral image" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

The invention belongs to the technical field of photovoltaic panel surface defect detection, and discloses a photovoltaic panel surface defect detection method and system based on physical property analysis. The method comprises the following steps: firstly, acquiring a surface temperature distribution image, surface deformation data, ultrasonic echo data, a spectral image, spectral characteristic data and eddy current signal characteristic data of the photovoltaic panel by respectively utilizing an infrared thermal imager, a laser speckle interferometer, an ultrasonic flaw detector, a visible light multi-band imager and eddy current detection equipment; various abnormal regions such as temperature, deformation, ultrasonic echo, spectral characteristics and eddy current signals are determined; and then determining defect positions by integrating various abnormal regions, and determining the types and sizes of the surface defects of the photovoltaic panel by combining various data corresponding to the defect positions. According to the method, accurate positioning and identification of the surface defects of the photovoltaic panel are realized through a multi-physical property detection means, and the detection accuracy and reliability are effectively improved.

The invention relates to the technical field of defect detection, and discloses an integrated circuit mask plate defect multispectral cooperative detection method, which comprises the following steps: carrying out preliminary scanning on a mask plate by using a multispectral camera, selecting an optimal wave band combination, switching to a target wave band through a liquid crystal adjustable optical filter, and obtaining a high-confidence multispectral image; the optical path offset is calculated through the linear relation, the deflection angle of the micro-mirror is dynamically adjusted, and images are collected for the second time after imaging offset is compensated; image filtering and registration are completed in the FPGA, and a high-contrast fusion image is generated; if the confidence coefficient is insufficient, triggering a wave band reselection mechanism to recheck; dynamically optimizing a defect type-spectrum mapping table based on real-time detection data; when the environment fluctuation exceeds the limit, an anti-interference mode is automatically switched, and micro-mirror calibration is triggered; and monitoring performance degradation of the optical assembly in real time, switching redundant optical paths, cleaning damaged parts, triggering manual reinspection when conflicts occur, and finally generating a visual report. According to the invention, the detection accuracy of the integrated circuit mask plate can be improved.

The invention belongs to the technical field of industrial measurement, and discloses a pump machine metal shell size detection method based on image analysis, which comprises the following steps: synchronously acquiring image data of a pump machine metal shell in a visible light wave band and a near-infrared wave band; the contrast ratio of image data is optimized through a dynamic exposure control technology, and a high-dynamic-range multispectral image is obtained; three-dimensional geometric information of the pump machine metal shell is obtained, registration fusion is carried out on the three-dimensional geometric information and the high-dynamic-range multispectral image, a dense three-dimensional point cloud model with multispectral textures is constructed, and a multi-frequency heterodyne algorithm is adopted to process a high-reflection area in the dense three-dimensional point cloud model; the method comprises the following steps: collecting a multi-angle reflection image of a pump machine metal shell by rotating a linear polarizer, calculating a Stokes vector to extract a diffuse reflection component, carrying out diffuse reflection component constraint and brightness suppression on a high-reflection area based on a CIE-Lab color space, and outputting a dense three-dimensional point cloud model after texture enhancement; and the size detection precision of the pump machine metal shell is improved.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

Disclosed in the present invention are a hyperspectral calculation imaging method and system based on multi-domain fusion of spatial, spectral and frequency domains, and a medium. The method of the present invention comprises: using a two-dimensional offline discrete cosine transform (DCT) to convert an RGB image Y into a frequency domain to obtain a frequency domain feature map Yfreq; extracting a frequency information image (I) from the frequency domain feature map Yfreq; using a two-dimensional offline inverse discrete cosine transform (IDCT) to transform the frequency information image (I) into a spatial domain to obtain a frequency information image Xfreq of the spatial domain; and fusing the frequency information image Xfreq of the spatial domain into the spatial-spectral domain features of the RGB image Y to generate a hyperspectral image (II). The present invention aims to solve the problems of poor detail information and low reconstruction accuracy of hyperspectral images in existing hyperspectral calculation imaging, and realizes high-fidelity reconstruction of a target spectrum.

The invention provides a hyperspectral image open set spectral spatial feature extraction and classification method, and aims to improve the classification precision of a known category and an unknown category in a hyperspectral image and the robustness of a classification model. According to the method, input data are preprocessed through fractional Fourier transform, and rotation analysis of a signal time-frequency plane is achieved. Multi-scale features of multi-branch cavity convolution are fused through an enhanced spectrum space residual module, deep separable convolution enhancement nonlinear representation of a lightweight convolution enhancement block is combined, and self-adaptive separation and weighted fusion of high / low frequency features are realized by innovating a dual-frequency enhancement module. Proposing a class perception comparison loss function, integrating anchoring loss, triple loss and regularization terms, and collaboratively optimizing intra-class compactness, inter-class separability and an open set decision boundary. The model framework provided by the invention is combined with the improved loss function, so that the classification precision of the known class and the unknown class of the hyperspectral image is remarkably improved.

The invention relates to the technical field of photovoltaic power station operation and maintenance, and discloses a photovoltaic power station intelligent operation and maintenance and fault early warning system. A multi-band image fusion algorithm and a temperature gradient enhancement algorithm are applied to identify composite defects; establishing a defect interaction influence model to analyze the synergistic effect of different defects; constructing a defect evolution prediction algorithm to predict a defect development trend; and developing a defect-performance mapping model and a processing priority algorithm to optimize a maintenance decision. According to the method, the defect detection sensitivity and accuracy are improved, accurate identification and evolution prediction of composite defects are realized, a prospective maintenance decision is effectively supported, the operation and maintenance efficiency of a photovoltaic power station is remarkably improved, the service life of the power station is prolonged, and the safety risk is reduced.

The invention belongs to the technical field of image processing, and particularly relates to a cord fabric production defect identification and analysis system based on machine vision, which comprises a multispectral imaging module, an image preprocessing module, a target detection module, a result verification module, a weight correction module and a control response module, the method comprises the following steps: acquiring a multispectral image sequence of each partition of a cord fabric forming surface, combining motion compensation, geometric calibration and noise filtering to improve preprocessing precision, and generating a composite spectral image by adopting adaptive weight fusion on the basis of considering an image signal-to-noise ratio and texture definition; and detecting defects based on frequency division domain texture and morphological gradient parameter bimodal matching, further verifying detection credibility through defect sensitive spectral image feature enhancement degree, dynamically correcting a fusion weight and performing iterative optimization, and finally triggering a production control instruction according to a detection result. And closed-loop management of high-precision defect detection and production control linkage of the cord fabric is realized.

The invention discloses a printed circuit board defect detection system. The image acquisition module is used for acquiring a multispectral image of a printed circuit board to be detected, and the image preprocessing module is used for denoising, enhancing and normalizing the acquired image. The feature extraction module extracts geometric and spectral information from the preprocessed image, including the shape and layout structure of the circuit. The deep learning module processes the extracted preliminary geometric and spectral information, and the basic network sub-module performs feature extraction through a pre-trained convolutional neural network to generate high-level features. And the self-adaptive adjustment sub-module carries out fine adjustment on the high-level features through defect sample data with labels, and generates specific features for identifying and classifying various defects of the printed circuit board. The defect identification and classification sub-module combines high-level features and specific features to realize accurate detection of short circuit, open circuit, insufficient etching, welding defects and the like. And the visualization module is used for marking the position and the type of the defect and generating a detection report.

The invention provides a farmland disease and pest monitoring method and device and a storage medium, and relates to the technical field of agriculture, the method comprises the steps: dividing a farmland into a plurality of sub-regions, obtaining an infrared spectrum image, and segmenting the infrared spectrum image into corresponding sub-images; and calculating a vegetation stress index based on the reflectivity of the insect pest related characteristic wave band, and screening high-risk sub-regions. Risk plants are randomly extracted in a high-risk area, physiological parameters of leaves are collected, physiological health indexes of the leaves are constructed, and dynamic correction is carried out through an environment regulation coefficient according to environment data. Using the trained network model to identify the risk sample leaf scab, and calculating the scab area index. And finally, fusing the corrected leaf physiological health index and the scab area index, calculating a normalized risk value through a risk assessment function, and dividing the normalized risk value into four risk levels. Through multi-stage fusion of spectral analysis, physiological parameter correction and deep learning scab detection, accurate early warning of diseases and insect pests is realized, and the monitoring efficiency is improved.

The invention discloses a three-dimensional panoramic intelligent monitoring method and device for a coal conveying gallery, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: obtaining initial point cloud data and an initial multispectral image of the coal conveying gallery through a rotary laser radar, a high-dynamic-range camera and an infrared thermal imager; inertial measurement units are arranged in the devices to collect vibration data, and the vibration data are used for carrying out vibration compensation on point cloud and images to generate target point cloud data and target multispectral images; thirdly, performing three-dimensional modeling and multispectral texture mapping on the coal conveying gallery based on the data to obtain a target three-dimensional model; and finally, mapping equipment, personnel and environment data into the three-dimensional model in real time, displaying each piece of state information, and realizing comprehensive monitoring. According to the application, high-precision three-dimensional panoramic monitoring can be realized in a complex vibration environment of the coal conveying gallery, and the safety management level and the operation and maintenance efficiency of the coal conveying gallery are effectively improved.

The invention discloses a multi-element historic building maintenance and restoration method based on three-dimensional laser scanning, and relates to the technical field of computer processing, and the method comprises the following steps: S01, obtaining the three-dimensional point cloud data of a historic building foundation through a ground laser scanner, the basic three-dimensional point cloud data comprises geometric information of a building facade, a ground structure and a near-ground decoration component; s02, an unmanned aerial vehicle carries a multi-line laser scanning device to scan the top and the high-altitude structure of the building, supplementary point cloud data is generated, and the millimeter-level precision three-dimensional coordinates of the pitched roof, the tower eave and the cornice bracket are covered with the supplementary point cloud data; and S03, synchronously acquiring surface texture data of the historic building by adopting multispectral imaging equipment. According to the method, the millimeter-level precision of three-dimensional reconstruction of the historic building is realized through multi-source data fusion and a hierarchical optimization algorithm, and the maintenance decision efficiency is improved by combining automatic disease recognition and a multi-modal analysis technology.

The invention relates to the technical field of strategy analysis, and discloses an alpine grassland rapid restoration strategy analysis method, system and device and a medium. The method comprises the following steps: performing quality evaluation and restoration on an alpine grassland multispectral image to obtain a standard image set; spectral texture features are extracted, degradation classification is carried out, and a feature set and a score table are obtained; evaluating the degradation degree in combination with multi-dimensional indexes, and generating a distribution matrix and an index set; forming a repair scheme library and a parameter list through case analysis and parameter optimization; analyzing a restoration effect in combination with environmental data to obtain an index matrix and an evaluation table; and optimizing the monitoring data to obtain a strategy scheme and an adjustment parameter set. According to the method and the device, the degradation conditions of alpine grassland of different types and different areas are accurately identified, the restoration strategy can be dynamically adjusted according to the real-time monitoring data, and the restoration effect is improved.

The invention relates to a multispectral image and hyperspectral image fusion method and system, and belongs to the field of deep learning and signal processing, and the method comprises the steps: carrying out the coding and feature extraction of a multispectral image and a hyperspectral image through employing a coder based on a spectrum self-attention mechanism; the hyperspectral image features and the multispectral image features obtained by the encoder are input into a space-spectrum fusion module, deep fusion of the spatial dimension and the spectral dimension is carried out, and unified feature representation is obtained; inputting unified feature representation obtained by fusion into a decoder based on a spectrum self-attention mechanism, inputting layered features passing through a graph-based spectrum sensing module into a corresponding decoder symmetric layer, and then decoding and outputting to obtain a spectrum image with high spatial resolution and a spectrum image with high spectral resolution; through the above strategy, full fusion and complementation of spectral image space information and spectral information are ensured, and the method has important value for research and application of MHIF.

The invention discloses a multi-modal thermal imager intelligent monitoring method based on image processing. The method comprises the following steps: S1, performing dual-band synchronous imaging; s2, carrying out cross-modal image registration; s3, multispectral image fusion: performing smoothing processing on the infrared and visible light images through anisotropic diffusion to obtain a main structure layer image, calculating to obtain an infrared and visible light detail layer image, and linearly combining the detail layer image through PCA principal component weight, and finally, combining the detail layer image and the main structure layer image according to the self-defined weight to obtain a final fusion image. And S4, gas leakage detection is carried out, so that gas leakage detection and early warning can be realized. According to the invention, a gas enhancement algorithm is adopted to realize real-time detection of leaked gas, interference of most background noise can be eliminated, and the real-time performance and reliability of the system are improved.

The invention belongs to the technical field of prefabricated box girder crack detection, and particularly relates to a prefabricated box girder crack dynamic detection system and a prefabricated box girder crack dynamic detection method. The crack initiation position is efficiently identified from a single-source spectral image by utilizing spectral reflectivity difference, so that the crack detection efficiency is remarkably improved, and meanwhile, crack information and stress data are subjected to correlation analysis to determine a critical stress distribution state causing crack initiation, so that effective prediction of potential cracks is realized, and the accuracy of crack detection is improved. And meanwhile, after the crack is initiated, the crack propagation speed and the crack propagation direction are continuously monitored, so that the real-time tracking of the crack dynamic evolution process is realized, and targeted crack propagation response measures are taken by utilizing the risk orientation obtained by performing risk assessment in combination with a crack propagation monitoring result, so that the further propagation of the crack can be effectively prevented.

The present invention relates to the technical field of image restoration, and in particular to a remote-sensing hyperspectral image super-resolution reconstruction method based on a hypergraph neural network, comprising: S1, acquiring a hyperspectral image, and preprocessing the hyperspectral image to obtain a training set and a verification set; S2, constructing a hypergraph neural network; S3, using training images in the training set to construct a three-layer hypergraph, and on the basis of the three-layer hypergraph, constructing hypergraph operators corresponding to the training images; S4, using the training images and the hypergraph operators corresponding to the training images to train the hypergraph neural network, and using a loss function to iterate network parameters of the hypergraph neural network, to obtain a trained hypergraph neural network; and S5, inputting, to the trained hypergraph neural network, low-resolution hyperspectral images to be reconstructed for reconstruction to obtain a high-resolution hyperspectral image. The present invention has an excellent reconstruction result.

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 provides a mountainous area tunnel crack intelligent identification and detection method based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: collecting multispectral image data, and carrying out the adaptive preprocessing to obtain an enhanced feature map; a double-flow network is combined with a space-channel cascade attention module to extract fusion features; constructing multi-scale feature representation through a feature association graph network; training the network by adopting a joint optimization target; and morphological processing and connectivity analysis are carried out to realize accurate identification and classification of the crack. According to the invention, the detection precision and the anti-interference capability of the tunnel crack in the complex environment are improved.

The invention relates to the technical field of mobile phone detection, in particular to an OLED mobile phone screen linear defect detection method and system. According to the method, the vibration time domain signal on the surface of the screen within the preset time period is obtained and does not depend on the display brightness of the screen, and the defect position is obtained through the abnormal signal interval, so that misjudgment caused by non-uniform brightness of the display image is avoided, the spectral data of each pixel is captured by using the spectral image, and the detection accuracy is improved. The influence of ambient light and display brightness can be effectively eliminated, defects are analyzed through the gradient magnitude of spectral data, misjudgment caused by light change and screen brightness difference can be reduced, abnormal pixels and normal pixels are distinguished by analyzing the gradient magnitude, subtle differences between the pixels can be recognized more accurately, and the accuracy of the detection result is improved. By acquiring the parameter features of each abnormal pixel, the characteristics of the defect can be described more comprehensively, so that the accuracy of defect identification is improved, the defect degree is evaluated through the parameter features, and more accurate defect degree evaluation can be obtained.

The invention relates to the technical field of image analysis, in particular to a concrete arch bridge crack semantic segmentation method based on multispectral imaging, and the method comprises the following steps: collecting a multi-period multispectral image through an unmanned aerial vehicle, segmenting a crack region through a K mean value of the multispectral image, extracting a multiband spectral vector sequence, and carrying out the sliding normalization to generate a principal axis spectral vector; and calculating a spectral vector included angle and a change rate mark jump point, screening boundary points by combining direction consistency and gradient scoring, extracting spectral lines Z-score standardization by time sequence alignment, evaluating discrete fluctuation, correcting an abnormal output concrete arch bridge crack binary segmentation map. According to the method, the reflection spectrum sequence is constructed through the multi-period multi-spectral image, the crack recognition precision is improved by combining the main shaft features and the sliding window, the texture interference is eliminated by using the spectral vector included angle change and the gradient score, the standardized spectral line time sequence model is generated, the boundary positioning accuracy is enhanced, the noise interference is reduced, and the long-term monitoring of the structural damage is supported.

The invention discloses an intelligent traditional Chinese medicine quality detection method and system based on deep learning, and relates to the technical field of medicine health, a high-resolution industrial camera and a multispectral imager are used for collecting multi-angle RGB images and spectral images of different wavebands, and an oil cell aggregation area is identified based on the images. And multi-dimensional detection is carried out from the physical structure and microcellular level, so that the quality detection accuracy of the traditional Chinese medicine decoction pieces is guaranteed in multiple aspects. And deeply analyzing the distribution condition of the oil cells according to the identified oil cell aggregation region, and generating judgment information containing a distribution result and an acid range. The judgment information can accurately reflect the abnormal conditions of the to-be-detected traditional Chinese medicine decoction pieces distributed for the corresponding production places under the current batch, a more comprehensive and detailed basis is provided for traditional Chinese medicine quality evaluation, and through the analysis, the quality difference of the traditional Chinese medicine decoction pieces in different production places can be effectively distinguished, and a deterioration risk signal is generated according to a distribution result.

The invention provides a radiotherapy patient skin injury real-time detection record management system based on image processing, and belongs to the technical field of radiotherapy patient skin injury real-time detection. Comprising a multispectral image acquisition module for imaging the skin of a patient at the same visual angle by adopting different spectrums to obtain three spectral images including near-infrared imaging, infrared imaging and ultraviolet imaging; a first identification module is used for fusing three spectral images to obtain an image data set, and a second identification module is used for constructing an epidermis injury coefficient # imgabs0 #, a dermis injury coefficient # imgabs1 # and a subcutaneous injury coefficient # imgabs2 # of the ith patient and evaluating the epidermis injury coefficient # imgabs0 #, the dermis injury coefficient # imgabs1 # and the subcutaneous injury coefficient # imgabs2 # respectively. A skin injury evaluation model is constructed, the comprehensive injury area ratio is calculated, the classification of injury levels is realized, data support is provided for a personalized treatment scheme, and a powerful auxiliary diagnosis tool is provided for a medical institution.

The invention discloses a wharf shoreline terrain reconstruction method based on remote sensing data, and belongs to the technical field of three-dimensional terrain reconstruction. The method comprises the following steps: acquiring a multi-temporal multispectral remote sensing image, a synthetic aperture radar image, a laser radar point cloud and synchronous tidal water level data of a target wharf area; processing the multispectral image to generate an initial land and water segmentation map; carrying out tide correction by utilizing the tide water level data, fusing the multi-polarization characteristics of the synthetic aperture radar image, and correcting the ground feature type to obtain an accurate land and water boundary diagram; processing the laser radar point cloud to generate a digital surface model, extracting a shoreline contour with sub-pixel-level precision from the digital surface model, and endowing the shoreline contour with an elevation value; and superposing the shoreline contour with the elevation with the multispectral image to generate a three-dimensional terrain model. According to the method, the problems of low terrain reconstruction precision, large tide influence and insufficient automation degree of a traditional method in a complex wharf environment are solved, and high-precision and high-efficiency wharf shoreline three-dimensional automatic reconstruction is realized.

The invention discloses a high-resolution hyperspectral image reconstruction method based on wavelet domain feature fusion and contrast learning, and belongs to the technical field of image fusion and super-resolution reconstruction. The method comprises the following steps: constructing a fusion network model comprising a wavelet transformation module, a cross-modal feature fusion module, a high-frequency contrast learning module and an image reconstruction module; performing end-to-end supervised training by using a training data set containing the low-resolution hyperspectral image and the high-resolution multispectral image; and after training is completed, inputting a test image pair to realize image reconstruction. According to the method, the detail retention capability is improved by combining wavelet decomposition and a directional fusion mechanism, the cross-modal high-frequency feature alignment capability is enhanced through comparative learning, a fusion image with high spatial resolution and high spectral consistency is finally generated, and the method is suitable for multi-modal image reconstruction tasks such as remote sensing, medical and natural images.

The invention provides a hyperspectral snapshot compression imaging method and system based on a space-spectrum prior decoupling model, high-quality reconstruction is realized through decoupling optimization and a deep expansion network, and the method comprises the following steps: constructing a training data set containing a compression measurement image and a corresponding reconstruction spectrum; establishing an objective function fusing space and spectrum prior, converting the objective function into constrained optimization, and converting the constrained optimization into three sub-problems of linear reconstruction, space prior and spectrum prior by adopting a semi-quadratic splitting method; a deep expansion network is designed to alternately solve sub-problems: a linear sub-problem is solved through analysis, and a space / spectrum sub-problem is subjected to implicit prior modeling through a private network, so that end-to-end reconstruction is realized; a mixed loss function is adopted to optimize model parameters, and images can be reconstructed in real time after training is completed. Space and spectrum prior decoupling is carried out, space structure details and spectrum features are respectively captured through an independent network architecture, the problem of mutual interference of joint modeling in a traditional method is solved, and high-quality spectrum image reconstruction is realized.

The invention discloses a micro-channel aluminum flat tube appearance detection method based on image processing, and relates to the technical field of appearance detection.According to the method, more comprehensive information acquisition is carried out through the multispectral imaging technology in combination with RGB, polarized light, infrared spectrum images and ultraviolet spectrum images, and surface temperature difference changes are detected through infrared spectrums; image preprocessing is carried out through contrast-limited adaptive histogram equalization and bilateral filtering, uneven illumination is inhibited while edge details are kept, the contrast of corrosion defects is improved, details of a low-resolution area are enhanced through a super-resolution reconstruction technology, edge features of a corrosion area are further optimized in combination with a histogram in the gradient direction, and the edge features of the corrosion area are further optimized. The identification capability of small-scale defects is improved; an improved YOLOv3 defect detection algorithm is introduced, multi-scale feature extraction is adopted, and a self-attention mechanism is introduced, so that the model pays more attention to a tiny corrosion area, and meanwhile, the detection precision of a small target is enhanced in combination with focus loss and regression loss.

The invention relates to an operation auxiliary system and method based on image processing, and the method comprises the steps: obtaining a preoperative medical image of a patient, collecting a multispectral imaging medical image of a surface structure, blood flow distribution and tissue elasticity of an operation region in real time, carrying out the image feature extraction after preprocessing, obtaining a preoperative image feature and an intraoperative multispectral image feature, and carrying out the image feature extraction; constructing a biomechanical organ model, updating tissue deformation parameters of the biomechanical organ model, performing space-time alignment on the intraoperative multispectral image features and the biomechanical organ model, generating a three-dimensional enhancement model including vascular topology, lesion boundary and elastic distribution, and superposing the three-dimensional enhancement model to a real-time surgical field image to obtain a real-time surgical field image. Marking the relative position of the surgical instrument and the risk area, generating an intervention signal when the instrument is close to a critical safety distance threshold value, and performing surgical operation by a doctor under assistance. Multi-dimensional intraoperative information is obtained through multi-spectral imaging to construct a high-precision dynamic biomechanical model, and precise navigation and active intervention are achieved in combination with the augmented reality technology.

The invention relates to the technical field of road pavement disease recognition, in particular to an intelligent road pavement disease recognition method and system based on a multispectral image, and the method comprises the steps: obtaining road pavement image data collected by a multispectral camera carried by an unmanned aerial vehicle; generating a registered multispectral fusion image; performing enhancement processing on the multispectral fusion image based on a dark channel algorithm to generate an enhanced image; performing shallow feature extraction and deep feature extraction on the enhanced image according to a convolutional neural network to generate an initial disease extraction image; performing segmentation processing on the initial disease extraction image through adaptive threshold segmentation to generate a binary image; performing morphological processing on the binary image to generate a candidate disease image; extracting edge information of a disease area from the candidate disease image based on an instance segmentation technology, and generating a segmented image; the segmented image is subjected to crack refining processing, a crack refined image is generated, and more comprehensive disease information support is provided for collection and fusion of multispectral data.