123 results about "Binary segmentation" patented technology

The invention discloses a remote sensing image cultivated land segmentation method and system fusing context and boundary perception, and belongs to the technical field of remote sensing image processing and agricultural information. Constructing a cultivated land segmentation initial model composed of a backbone network, a feature enhancement module, a multi-scale feature fusion de-wharf module and a mask prediction module; training set data are input into the initial model, a composite loss function value is calculated, back propagation is executed, and a cultivated land segmentation model with boundary sensing ability is obtained through multi-round iterative optimization; and inputting the remote sensing image into the trained cultivated land segmentation model, and outputting a binary segmentation image representing the cultivated land position. Visual state space modeling and large receptive field convolution are combined, deep and shallow layer information is fused through feature injection, boundary perception supervision and composite loss are introduced, cultivated land boundary discrimination is improved, remote sensing image cultivated land high-precision extraction is achieved, and the method is suitable for agricultural interpretation and monitoring.

The invention provides a dermatoscope image segmentation method, and relates to the technical field of medical image processing, and the method comprises the steps: inputting a to-be-segmented image into an improved encoder module, carrying out the multi-scale feature information extraction, and obtaining a deep advanced semantic feature map; inputting the deep advanced semantic feature map into the bridging module, and performing context information modeling and cross-scale feature fusion to obtain a fused feature map; inputting the fused feature map into the decoder module, and carrying out layer-by-layer up-sampling and feature integration to obtain a reconstructed feature map; inputting the reconstructed feature map into the boundary sensing double-branch module, and performing collaborative feature processing; wherein the main branch outputs a binary segmentation mask, and the auxiliary branch is used for measuring a sign distance function diagram to provide boundary perception supervision; obtaining a binary segmentation mask output by the main branch as a segmentation result; compared with an existing model, the optimized dermatoscope image segmentation model is higher in recognition accuracy.

The invention discloses a chip surface defect detection method, electronic equipment and a readable medium. The method comprises the following steps: aligning an ROI (Region of Interest) of a reference image with a test image through affine transformation; obtaining a gray average value of the ROI of the reference image and the ROI of the aligned test image, and carrying out gray stretching normalization processing on the test image based on the gray average value; respectively carrying out mean filtering on the reference image and the test image after normalization processing, and carrying out differential operation to obtain a differential image; performing threshold-based binary segmentation on the difference image, extracting surface detection candidate areas of bright defects and dark defects, performing morphological corrosion operation on the surface detection candidate areas to suppress noise, and generating a final defect mask; and mapping a defect area in the final defect mask back to the original test image coordinate system through inverse transformation of affine transformation. According to the method, high-precision smudginess defect extraction of the IPM chip area under the background of complex brightness can be realized, and the method is suitable for various complex packaging working conditions.

The invention discloses a low-altitude remote sensing ground feature element extraction method fused with a DSM side adapter. According to the method, visual features are extracted by taking a pre-training visual large model of frozen parameters as a trunk, and a trainable DSM side adapter is constructed in parallel to extract geometric features of elevation of a ground object target; a cross-modal attention fusion mechanism is utilized, visual features are used as queries, elevation geometric features are used as key values, height information injection is dynamically guided, and content-aware adaptive modal fusion is achieved. Besides, a parallel prompt decoding strategy is adopted, a virtual task batch is constructed by using a batch stacking technology, and multi-category semantic segmentation is reconstructed into a high-dimensional parallel prompt-driven binary segmentation task, so that the limitation of a large model native normal form is broken through on the premise of less structural modification, and the real-time performance of the system is improved. And end-to-end fine tuning is carried out by combining a channel mutual exclusion and competition mechanism. According to the method, the ground feature element extraction precision and the boundary integrity in a complex scene are remarkably improved, and the method has the advantages of low training cost, high generalization and flexible deployment.

The invention relates to a high-precision crack segmentation method based on double-flow visual basic model collaboration, belongs to the technical field of computer vision and deep learning, and aims to solve the problems that an existing crack segmentation model is poor in generalization under a complex background, fine cracks are missed and disconnected under high resolution, and the fine tuning cost of a large model is high. The method comprises the following steps: firstly, preprocessing an acquired infrastructure surface image into standard input of 1024 * 1024 pixels, and extracting features by adopting a double-flow encoder containing parallel SAM3 and DINOv3 double branches; freezing double-bone intervention training parameters, and embedding a lightweight adapter to complete efficient fine tuning of the parameters; double-branch multi-scale features are extracted, and multi-scale splicing and dimension reduction fusion are completed after channel alignment; and outputting a pixel-level crack binary segmentation result through a lightweight decoder based on depth separable convolution. According to the method, segmentation precision, cross-domain generalization ability and training deployment cost are considered, and the engineering landing requirement of infrastructure health monitoring is met.

The invention discloses an image fine structure intelligent detection algorithm based on a double-branch encoder. The method comprises the following steps: firstly, constructing an image segmentation system comprising a feature prior branch based on a lightweight pre-training model, a fine structure extraction branch based on a structure perception visual state space module, a cross-modal fusion module and a multi-scale feature complementary mapping decoder; secondly, respectively inputting the original image into a feature prior branch and a fine structure extraction branch of an image segmentation system for feature extraction and preliminary fusion, then performing second-stage fusion on features after preliminary fusion through a cross-modal fusion module, and finally inputting the features into a multi-scale feature complementary mapping decoder to obtain a binary segmentation image; through the design of a feature prior branch and a fine structure extraction branch, the generalization ability of the model in a few-sample scene is enhanced, and the long-distance dependence and complex irregular form of the fine structure are efficiently captured with lower calculation cost; and a two-stage cross-modal fusion mechanism improves the characterization capability of the model on fine structure features.

The invention belongs to the technical field of water ecology risk early warning, and provides an algal bloom risk remote sensing intelligent identification method and system, and the method comprises the steps: obtaining remote sensing image data, meteorological data and water quality data of a target region, and carrying out the preprocessing; performing frequency domain feature extraction to generate a three-dimensional frequency domain feature vector; fusing the global feature representation obtained by modeling and the generated three-dimensional frequency domain feature vector by using a multi-task deep learning network to obtain a feature map; obtaining an algae bloom binary segmentation probability graph and a continuous value distribution graph of chlorophyll a concentration based on the characteristic graph; generating an image semantic embedding vector by utilizing a semantic embedding head, and performing semantic alignment on the image semantic embedding vector by adopting a pre-trained knowledge graph to generate a research and judgment information text; and performing cross validation on the study and judgment information text, the algae bloom binary segmentation probability graph and the continuous value distribution graph of the chlorophyll a concentration to obtain a final algae bloom risk judgment result. According to the invention, the algal bloom risk remote sensing intelligent identification is realized.

The invention discloses a driver direct view calculation model and a test method. Comprising the following steps: firstly, acquiring virtual test scene data corresponding to a to-be-tested vehicle; and inputting the virtual test scene data into the trained calculation model. The calculation model comprises a view region segmentation sub-model and a visible volume calculation sub-model, and a visible region and a shielding region in a standard evaluation volume in the virtual test scene data are accurately segmented through the view region segmentation sub-model to obtain a corresponding binary segmentation mask. And extracting spatial distribution characteristics strongly related to the visible volume from the segmented binary mask through a visible volume calculation model, fusing, screening and compressing the extracted spatial distribution characteristics, and finally mapping the spatial distribution characteristics into four types of visible volumes of the near side, the front side, the far side and the total of the tested vehicle. Therefore, a large entity test scene is not needed, and the test efficiency is improved. And when the standard is updated, only a data set needs to be updated and model parameters need to be calculated, and the overall architecture does not need to be redeveloped.

The invention discloses a prostate MR image segmentation method and system based on multi-level context aggregation, and the method comprises the steps: segmenting a prostate MR image through a trained prostate MR image segmentation model, and obtaining a segmentation result. The model is based on an improved U-shaped architecture and comprises a multi-level coding module, a jump connection processing module, a bottleneck layer, a multi-level decoding module and a segmentation head. The multi-level coding module extracts image features of different levels, and the jump connection processing module generates enhanced features through combination of hierarchical expansion convolution and an improved Transform architecture; the bottleneck layer decouples the bottom layer features into foreground / background features, and outputs a feature map through local window attention weighted fusion; and the multi-level decoding module gradually recovers the resolution through residual convolution and bilinear up-sampling, and finally outputs a binary segmentation result through a segmentation head. According to the method, the accuracy and robustness of prostate MR image segmentation are improved through hierarchical feature aggregation and attention fusion.

The invention provides a cowshed drivable area detection method based on fusion of a laser radar and a monocular camera, and the method comprises the steps: synchronously collecting real-time point cloud data and image data, carrying out the matching of the real-time point cloud data and a point cloud map constructed offline, and carrying out the calculation to obtain the pose state of a vehicle in a current map; the method comprises the following steps: inquiring and acquiring key points of a global induction area around a vehicle from a priori map constructed offline, and performing spatial mapping to form an image region of interest; pixel-level classification is carried out through a pre-constructed lightweight semantic segmentation model so as to output and obtain a binary segmentation mask; and mapping the binary segmentation mask from the image pixel coordinate system to the vehicle coordinate system through inverse perspective transformation, and generating a drivable area map under the vehicle coordinate system. According to the invention, through the core thought of priori map guidance, semantic fine recognition and coordinate system unified restoration, the drivable area detection of the cowshed is solved, and a basis is provided for a subsequent path planning module.

The invention relates to the technical field of medical image processing, in particular to an intestinal polyp segmentation method and system based on a spiral scanning state space model, and the method comprises the following steps: S1, obtaining and preprocessing an image; s2, performing multi-scale context feature coding; s3, self-adaptive frequency domain feature enhancement is carried out; s4, performing decoding and segmentation prediction; and S5, carrying out threshold processing to generate a binary segmentation mask image. According to the method, the boundary integrity of an irregular target is kept through a dynamic multi-focus spiral scanning strategy, and the adaptive frequency domain filtering module is introduced to enhance the discriminability of key features, so that the segmentation precision and robustness in complex medical image tasks such as intestinal polyp and the like are comprehensively improved.

The present application relates to the technical field of image processing, in particular to an automatic snow removal method and system based on image enhancement processing, comprising the following steps: collecting an original snow image of a road surface, decomposing the image brightness component by using a multi-scale Retinex algorithm, decomposing into a low-frequency component and a high-frequency component, and generating an enhanced image; based on the enhanced image, calculating the joint gradient of saturation and brightness in the pixel point HSV space, and generating a snow region binary segmentation image by using an improved Otsu double threshold method; determining the snow compactness level according to the texture uniformity; driving an execution mechanism to spray snow remover according to path coordinates, and adjusting the snow remover spraying concentration in combination with the compactness level. The present application improves the snow boundary definition and the internal texture recognizability of patches, lays a high-quality image foundation for subsequent segmentation and analysis, and enhances the adaptability and stability of the system under natural light conditions.

This application provides a method and apparatus for three-dimensional surface reconstruction of medical images based on diffusion least squares, belonging to the field of medical image processing. The method includes: edge extraction based on a binary segmentation mask of a three-dimensional image to determine multiple edge voxels of the three-dimensional image; based on the edge voxels, selecting multiple sampling voxels from the voxels of the three-dimensional image; performing least squares fitting on multiple local coordinate data of the multiple sampling voxels to determine multiple fitting parameter values ​​and calculating the corresponding residual values; if the residual value is greater than the fitting error threshold, determining the global coordinate data of the edge voxels through data mapping based on the multiple fitting parameter values; and obtaining a three-dimensional surface mesh through Poisson surface reconstruction and mesh iterative deformation based on the multiple global coordinate data.

The application discloses a method, system, device and medium for recognizing point fluorescence signals in cell nuclei. The method comprises inputting a pre-processed tissue slice scanning image into a cell nucleus segmentation network model to obtain a binary segmentation mask image; determining a target cell nucleus mask based on the binary segmentation mask image; multiplying a point fluorescence signal channel with the target cell nucleus mask to obtain a multiplication feature result image; inputting the multiplication feature result image into a point fluorescence signal recognition model to obtain a signal point heat map and a preliminary segmentation mask; fusing the signal point heat map, the preliminary segmentation mask and the point fluorescence signal channel to obtain a fusion feature image; inputting the fusion feature image into a signal point instance segmentation model to obtain a plurality of single signal point instances; and determining a target ACD score of the tissue slice scanning image based on the plurality of single signal point instances. The application can improve the accuracy, stability and efficiency of recognizing point fluorescence signals in cell nuclei.

A method for online temperature measurement target localization of furnaces and kilns based on dual-light fusion includes the following steps: a. Simultaneously acquiring visible light image sequences and thermal imaging image sequences of the surface of a rotating furnace and kiln through a dual-light data acquisition module; b. Processing the visible light image sequences and thermal imaging image sequences to generate a dense non-rigid deformation field, and applying the non-rigid deformation field to obtain a pair of geometrically corrected and spatially precisely aligned corrected image pairs; c. Fusion processing of the corrected image pairs, real-time sensing and adaptation to the interference level of different modal signals by the on-site environment, dynamically adjusting the fusion strategy, and generating a binary segmentation mask identifying thermal anomaly regions on the furnace and kiln surface; d. Using a kinematically aware spatiotemporal graph Kalman network (K-STGKN), performing state tracking and future state prediction on the thermal anomaly targets identified in the segmentation mask. This invention achieves high-precision, highly robust, and predictive localization and analysis of thermal anomalies on the surface of rotating industrial furnaces and kilns.

The application provides a kind of unsupervised hyperspectral image classification method based on mixed space spectrum information, comprising the following steps: S1, obtains binary segmentation graph by entropy rate superpixel segmentation algorithm, applies binary segmentation graph on original hyperspectral image to obtain segmented superpixel block, converts input hyperspectral image into multiple homogeneous regions based on superpixel segmentation, removes redundant information and guides data purification;S2, optimize the redundant information in principal component domain by two-dimensional singular spectrum analysis method, enhance spatial spectral feature;S3, realize the unsupervised classification of large-scale hyperspectral image by anchor point graph clustering unsupervised classification method.The application is closer to actual engineering application compared with existing supervised classification method, can process larger image scale compared with existing unsupervised classification method, has the advantages of not needing prior information reference, high classification precision, fast classification speed and the like.

The present application belongs to the technical field of water ecological risk early warning, and provides an algal bloom risk remote sensing intelligent identification method and system, remote sensing image data, meteorological data and water quality data of a target area are acquired and preprocessed; frequency domain feature extraction is performed to generate a three-dimensional frequency domain feature vector; a multi-task deep learning network is used to fuse the global feature representation obtained by modeling and the generated three-dimensional frequency domain feature vector to obtain a feature map; based on the feature map, an algal bloom binary segmentation probability map and a continuous value distribution map of chlorophyll a concentration are obtained; a semantic embedding head is used to generate an image semantic embedding vector, a pre-trained knowledge graph is used to perform semantic alignment on the image semantic embedding vector to generate research and judgment information text; the research and judgment information text, the algal bloom binary segmentation probability map and the continuous value distribution map of chlorophyll a concentration are cross-validated to obtain a final algal bloom risk judgment result. The present application realizes algal bloom risk remote sensing intelligent identification.

The application discloses an image fine structure intelligent detection algorithm based on a double-branch encoder, and comprises the following steps: first, constructing an image segmentation system comprising a feature prior branch based on a lightweight pre-training model, a fine structure extraction branch based on a structure perception visual state space module, a cross-modal fusion module and a multi-scale feature complementary mapping decoder; then, inputting an original image into the feature prior branch and the fine structure extraction branch of the image segmentation system respectively to perform feature extraction and preliminary fusion; then, performing second stage fusion on the preliminarily fused features through the cross-modal fusion module; and finally, inputting the features into the multi-scale feature complementary mapping decoder to obtain a binary segmentation graph. Through the design of the feature prior branch and the fine structure extraction branch, the generalization ability of the model in a few sample scene is enhanced, and long-distance dependence and complex irregular morphology of fine structure are efficiently captured at a lower calculation cost. A two-stage cross-modal fusion mechanism improves the representation ability of the model to fine structure features.

The invention discloses a paper cup flaw inspection method based on an image detection technology, and the method comprises the steps: carrying out the binary segmentation of a paper cup image, extracting a paper cup region, fitting an outer ellipse, and enabling the outer ellipse to contract inwards for a specified size, and obtaining a top ellipse; fitting a bottom ellipse through contraction and edge scanning by taking the outer ellipse as a reference; automatically detecting the joints of the paper cups through image processing, and setting a joint area as a neglected area; the paper cup is divided into a top inspection area, a side inspection area and a bottom inspection area based on an outer ellipse, a top ellipse and a bottom ellipse, and defect inspection is performed by using various defect detection algorithms respectively. The method is high in universality, various paper cups and paper bowls can be inspected, manual setting of inspection areas by a user is avoided, in addition, by automatically extracting seams and ignoring seam areas, the seam areas are prevented from being detected as defects, the defect inspection accuracy is improved, and therefore the three defect detection methods can be comprehensively used, and the detection efficiency is improved. Different algorithms are suitable for defects of different forms, and missing detection is avoided as much as possible.

The application relates to the technical field of gene variation detection, in particular to a copy number variation detection method based on semi-supervised learning. The method comprises the following steps: obtaining read depth signals and mapping quality signals of each normal window of a reference genome sequence from alignment information of sequencing reads, correcting the read depth signals of all normal windows in terms of GC content bias, adopting a cyclic binary segmentation algorithm to divide all normal windows into segmented regions with uniform read depth signals, identifying copy number variation breakpoint positions in combination with a split read strategy, performing normalization processing on the mapping quality signals, performing smoothing and noise reduction processing on the read depth signals, labeling pseudo labels for corresponding segmented regions, performing clustering analysis on all segmented regions through an improved density clustering algorithm, integrating and determining the variation types of abnormal segmented regions, and outputting copy number variation detection results, so that efficient detection of copy number variation is realized, and the accuracy and reliability of the detection results are significantly improved.

The invention relates to a tunnel leakage water disease automatic identification method, device and equipment and a medium thereof. The method comprises the following steps: acquiring multi-source original data in a tunnel; preprocessing the multi-source original data to obtain multi-sensor data; the multi-source original data comprises visible light images, thermal imaging and point cloud data; dividing a tunnel section along the center line of the tunnel based on the multi-sensor data to obtain a tunnel section sequence; the tunnel section sequence comprises each tunnel section and the corresponding multi-sensor data; based on the tunnel section sequence, performing binary segmentation on the tunnel section to obtain a section binary segmentation mask; and based on the section binary segmentation mask, identifying the leakage water disease type of each tunnel section, and obtaining a disease classification result. By adopting the method, a unified section reference coordinate system can be established to track a disease development track, and further disease development can be accurately warned.

The application discloses a part surface quality detection method, device, equipment and medium, and relates to the technical field of intelligent manufacturing, which comprises the following steps: acquiring a part image containing surface information of a part to be detected, selecting a defect description text based on detection requirements, and constructing a defect detection task instruction; inputting the part image and the defect detection task instruction into a preset quality detection model to calculate a first affinity of the part image and a second affinity of the defect detection task instruction through each matching unit, selecting a target matching unit from each matching unit based on the first affinity and the second affinity; activating the target matching unit to generate an initial graphic-text matching graph; reading image edge information of the part image through the preset quality detection model to obtain diffusion modulation parameters, and iteratively processing the initial graphic-text matching graph based on the diffusion modulation parameters, and then outputting a binary segmentation image containing a defect prediction result. High-precision detection of the part is realized.

This application relates to a virtual binocular speckle stereo matching method based on local gray-level plane binary segmentation. It pertains to the fields of computer vision, 3D measurement, and stereo vision, and includes the following steps: S1: image loading and parameter settings; S2: local gray-level plane binary segmentation; S3: disparity calculation and sub-pixel optimization based on Hamming distance; S4: disparity map post-processing; sub-pixel interpolation is performed based on the matching cost curve to obtain disparity values ​​with sub-pixel accuracy, resulting in an initial sub-pixel disparity map; S5: depth map calculation and effective value filtering: the initial sub-pixel disparity map is filtered, consistency checked, and outlier removed to obtain an optimized dense disparity map; finally, combined with the calibration parameters of the virtual binocular system, the dense disparity map is converted into a depth map, and the result is visualized. This method has the advantages of high robustness, high accuracy and efficiency, and strong versatility.

The application provides a point cloud segmentation method, device and equipment of an angiogram and a medium. The method comprises the following steps: based on the position of each point cloud on the vessel centerline in the vessel binary segmentation image, a plurality of candidate paths of the vessel centerline are determined by using a depth-first traversal algorithm; based on the direction vector of each candidate path in the plurality of candidate paths and a preset reference direction vector, a candidate direction feature list is constructed; for each point cloud, based on the node type corresponding to the point cloud and the candidate direction feature list, the direction feature corresponding to the point cloud is determined, and the position coordinates of the point cloud and the direction feature corresponding to the point cloud are spliced to obtain the point cloud feature of the point cloud; finally, the point cloud feature of the point cloud is input into a pre-trained point cloud segmentation model to determine the point cloud segmentation result of the point cloud in the vessel centerline. Through the method and the device, the segmentation accuracy of the point cloud segmentation network for the vessel image is improved.

The invention discloses an automatic floor tile gap detection method and system based on image recognition, and the method comprises the steps: controlling an image collection module carried on a mobile platform to scan the surface of a floor tile along a preset path, and obtaining a floor tile image; performing image preprocessing on the floor tile image to obtain a preprocessed image; inputting the preprocessed image into a pre-trained slit segmentation deep learning model for pixel-level segmentation, and outputting a binary segmentation mask representing the slit position; and performing morphological closing operation on the binarized segmentation mask, extracting a center line of the floor tile gap, and determining a detection parameter of the floor tile gap according to the center line. The full-area automatic image acquisition of the surface of the floor tile is realized, the acquisition efficiency and the coverage integrity are greatly improved, and the problem of leak detection is effectively avoided. The method improves the precise quantification capability of the gap parameters, and facilitates the accurate determination of the detection parameters of the floor tile gaps.

The present application relates to the technical field of computer vision and road engineering, and particularly relates to a pavement crack lightweight segmentation and quantification method based on enhanced feature fusion. The method comprises the following steps: decoding and reading pavement images and performing verification, then performing grid division and cutting, and performing pixel normalization processing on the effective sub-images after cutting; a lightweight crack segmentation network is constructed; the preprocessed standard images are input into the lightweight crack segmentation network, and feature extraction, feature fusion, target detection and pixel segmentation are sequentially completed, and a crack detection frame and a binary segmentation mask are output; the lightweight crack segmentation network is trained, the optimal model is saved according to the iteration optimization of the verification set index; the segmentation mask output by the model is denoised, the crack geometric size is calculated, and the final detection quantification result is output. The method has the advantages that the small-scale crack recognition capability is improved, the crack length and width can be automatically and accurately measured, the crack size measurement error is small, and the method is suitable for large-scale pavement inspection operation.

The application provides a method for distinguishing the color of sub-group tobacco leaves, applied to the technical field of computer image processing, and comprises the following steps: obtaining a color classification sample image set of sub-group tobacco leaves, performing binary segmentation on the color classification sample of the tobacco leaves, and obtaining a binary image tobacco leaf area; performing coordinate return on the binary image tobacco leaf area, and generating a color image tobacco leaf area; extracting a pixel point Lab value set of the color image tobacco leaf area, combining a pure color pixel point Lab value, and calculating a pixel point contrast set; performing interval division on the pixel point contrast set, and generating a contrast interval division result; traversing the division result, and calculating an interval pixel proportion set; according to the interval pixel proportion set, drawing a proportion threshold point line graph, and then performing color distinction on a to-be-tested tobacco leaf image according to a voting mechanism. The method solves the technical problems that there is no standardized method for distinguishing the color of sub-group tobacco leaves in the prior art, manual classification has low efficiency and low classification accuracy, and is highly subjective.

The invention discloses a flood influence range extraction method and system based on a convolution and state space model, and belongs to the technical field of remote sensing image processing and disaster monitoring. Comprising the following steps: acquiring remote sensing image data of a to-be-detected area, and performing preprocessing; inputting the preprocessed remote sensing image into a multi-stage and multi-branch hierarchical feature extraction encoder; a channel attention and space attention mechanism of an encoder is extracted by using hierarchical features, and dynamic weighting and fusion are carried out on local and global features of different scales; and sending the fused multi-scale feature map into a decoder for up-sampling, and finally outputting a pixel-level binary segmentation mask of the flood influence range. The method has the advantages of being high in calculation efficiency, accurate in edge positioning and high in anti-interference capacity, and is suitable for actual services such as emergency monitoring, disaster situation evaluation and post-disaster reconstruction.

The invention relates to the technical field of image processing, and discloses a landslide surface crack three-dimensional evolution unmanned aerial vehicle image extraction and quantification method and system, and the method comprises the steps: carrying out the bundle adjustment of an ultrahigh overlapping degree aerial image of a landslide surface, and obtaining an image shooting position, an image attitude parameter and a sparse three-dimensional point cloud; constructing a high-precision digital earth surface model; performing digital mosaic on the aerial image with the ultrahigh overlapping degree to obtain an orthoimage; performing semantic segmentation on the orthoimage to obtain a crack binary segmentation map of the orthoimage so as to identify a main crack and a secondary crack; performing three-dimensional geometric parameter analysis on the main crack and the secondary crack to obtain a crack three-dimensional space attribute parameter; carrying out evolution trajectory tracking on the crack three-dimensional space attribute parameters to obtain evolution data so as to construct a visual map of the landslide surface; according to the invention, the efficiency of extracting and quantifying the three-dimensional evolution unmanned aerial vehicle image of the landslide surface crack can be improved.

The invention discloses a self-adaptive homogeneous pixel offset tracking glacier time sequence monitoring method and system, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a plurality of groups of dual-time-phase SAR image pairs of a glacier; performing image recognition on the dual-time-phase SAR image pair, and outputting a binary segmentation result of a glacier change boundary; a binary segmentation result is used as mask input of pixel offset tracking, and a self-adaptive homogeneous pixel matching window is constructed by distinguishing a changing area and a non-changing area in a pixel offset tracking window, so that matching windows of master and slave images of a dual-time-phase SAR image pair only contain homogeneous pixels with the same changing characteristics as a central pixel, and the matching windows of the master and slave images of the dual-time-phase SAR image pair only contain homogeneous pixels with the same changing characteristics as the central pixel. Normalization matching is carried out; and when the local SNR of the homogeneous pixel matching window is smaller than a preset signal-to-noise ratio threshold value, performing offset calculation by adaptively adjusting the size of the window in the row and column directions, and outputting LOS-direction and azimuth-direction two-dimensional timing sequence offset results. According to the method, the glacier change detection accuracy and the deformation calculation precision can be comprehensively improved.