221 results about "Geometric distortion" patented technology

The invention belongs to the technical field of engineering information intelligent processing, and relates to a highway bridge drawing multi-modal information extraction and semantic understanding method and system.The method comprises the steps that firstly, self-adaptive judgment is conducted on a vector drawing and a scanning drawing, geometric distortion correction, drawing frame and title bar positioning and layout segmentation are completed, and then the vector drawing and the scanning drawing are obtained; constructing a hierarchical document structure comprising texts, tables, images and two-dimensional drawing objects; then, a front-end target detection network and a rear-end cross-modal document understanding model are fused, and detection and relation reasoning of elements such as view blocks, labels, symbols and tables are achieved; and image-text feature alignment is further performed by using a visual coding network and a text coding network, structural description conforming to engineering semantics is generated by means of a multi-modal language model, an engineering parameter database is established, and parameter query and multi-modal question and answer output are supported. According to the method, the accuracy and efficiency of automatic acquisition and semantic understanding of the key information of the highway bridge drawing are improved.

The invention discloses a farmland furrow three-dimensional point cloud data correction method and system based on a laser radar, and relates to the technical field of three-dimensional image processing based on the laser radar, in particular to a three-dimensional image geometric correction technology. The problems that original point cloud data directly obtained under the dynamic operation condition of an existing agricultural machine has geometric distortion, and the shape of a furrow cannot be truly reflected are solved; meanwhile, the problems that furrow quality evaluation seriously depends on low-efficiency manual means, the adaptability of an existing automatic technical scheme to an unstructured field environment is insufficient, and robust, real-time and high-precision perception of a real three-dimensional shape of a furrow cannot be achieved are solved. According to the method, dynamic tilt correction is carried out through real-time acquisition of point cloud and agricultural machine attitude information, boundary feature points are extracted, boundary lines are fitted, and a three-dimensional point cloud set of a single furrow is reconstructed by fusing a self-adaptive clustering algorithm of spatial prior. The method is suitable for the fields of farmland tillage and soil preparation quality detection, precision agricultural management, agricultural machinery intelligent operation and the like under the dynamic operation condition of agricultural machinery.

The invention relates to a multi-modal perception fusion target following method and system. The method comprises the following steps: adopting an improved KCF algorithm to realize a closed-loop process of target tracking, multi-scale space construction, feature fusion, response calculation, optimal scale decision and model updating; designing a four-level shielding processing mechanism fusing motion prediction and depth verification, and realizing tracking recovery in a shielding scene through shielding judgment, motion prediction, fine search and template protection; constructing a target distance mapping function fusing geometric distortion correction and attitude compensation, and realizing high-precision distance estimation based on monocular vision; laser radar point cloud information is integrated, an obstacle threat degree model is constructed, and cooperative path planning of following and obstacle avoidance is realized in combination with an improved TEB algorithm; and designing a linear velocity control law and an angular velocity control law based on the distance deviation and the azimuth angle deviation, and driving the robot to complete target following motion. According to the invention, high-precision and robust following of the robot to the target can be realized.

The invention discloses a self-adaptive text extraction method and system based on artificial intelligence, and the method comprises the steps: carrying out the analysis of the document structure entropy of an example document set, quantifying the noise density, geometric distortion degree and background complexity of the example document set, and carrying out the self-adaptive selection of a preprocessing assembly line intensity grade according to the above; dynamically configuring image preprocessing parameters and AI recognition model parameters, and generating a recognition engine instance to output a preliminary recognition text; after regularized coarse screening extraction is carried out based on key field description, a multi-candidate generation strategy is started for low-confidence-coefficient candidate text fragments, a multi-person cooperative verification process is triggered for lower-confidence-coefficient fragments, finally all the fragments are processed through a text standardization module, and structured text extraction information is output. According to the method, accurate adaptation of processing intensity is achieved through document quality quantitative evaluation, the extraction accuracy and system robustness of complex heterogeneous documents are effectively improved through a multi-level confidence coefficient verification mechanism, and the identification error risk caused by image quality fluctuation or rule solidification is reduced.

The invention discloses a glomerular segmentation method based on multi-scale Mama and dynamic sampling, and relates to the technical field of medical images. According to the method, the input multi-scale features are efficiently represented by using a continuous state transition mechanism, the structural association information extraction capability among different scales is enhanced, in addition, the problem of geometric distortion in dynamic sampling is solved by introducing position coding into the dynamic sampling method and adding position constraint information, and the dynamic sampling efficiency is improved based on a U-Net model. An MS-Mama module is gradually added in the encoder stage, and an existing bilinear interpolation method is replaced by a position sensing dynamic sampling module; and then, serialized representation is performed on multi-scale features based on a selective state space mechanism, the dependence of Mama on specific scale features is reduced, the ability of extracting heterogeneous morphological features of lesion glomerulus is enhanced, the boundary positioning precision of lesion glomerulus is enhanced by adding position constraint information, and the geometric distortion problem in dynamic sampling is solved.

The invention discloses a multi-modal image registration method and system based on deformation adaptation and computer equipment, and the method comprises the steps: collecting a plurality of groups of multi-modal images, carrying out the gray standardization, and constructing a diversified registration data set; building a registration network model comprising a pyramid coding module, a deformation adaptive module, a cross-modal interaction module and a registration parameter estimation module; inputting an image pair into the modules in sequence, respectively extracting basic feature mapping, deformation feature mapping and interaction enhancement feature mapping, and finally outputting an estimation conversion parameter matrix; a training process is supervised through a preset loss function, optimal network parameters are selected, and a trained registration model is obtained; in practical application, an image pair to be registered is input into the trained model, a conversion parameter matrix is obtained, and image registration is completed. The multi-modal image registration performance can be effectively improved, and the method still has good robustness and adaptability especially under the condition that serious geometric distortion and significant modal difference exist.

The invention provides a single building three-dimensional reconstruction method based on improved semantic segmentation, and relates to the technical field of three-dimensional reconstruction. The method comprises the following steps: calculating pose parameters of a multi-view inclined image by using a multi-view image processing technology and removing geometric distortion influence; inputting the corrected image into a semantic segmentation module, extracting a building pixel region and labeling attributes, and generating a dichotomy attribute tag graph; performing depth estimation on the corrected image by using a DPT network model to generate a depth map; combining the dichotomy attribute tag map with the depth map to generate an initial point cloud; performing multi-view initial point cloud fusion by using an ICP algorithm to generate a global scene dense point cloud model; and according to the attribute category, automatically separating the building from the dense point cloud model of the global scene to obtain a point cloud model of a single building. According to the method, accurate recognition and three-dimensional reconstruction of the single building are realized, and the problems of mistaken extraction and over-extraction of the single building in an existing method are solved.

The invention relates to the technical field of unmanned aerial vehicle remote sensing detection, and discloses a change detection method and system for an unmanned aerial vehicle remote sensing image. The method comprises the following steps: acquiring a multi-temporal unmanned aerial vehicle remote sensing image data set containing high-resolution image data acquired at different times in a target area; carrying out geometric correction and radiation normalization processing on the data set to generate a standardized remote sensing image data set, and eliminating geometric distortion and illumination difference between images; then extracting multi-scale spatial features in the standardized remote sensing image data set, generating a spatial feature matrix containing texture, spectrum and structure information, and comprehensively capturing image details; inputting the spatial feature matrix into a time sequence feature fusion network, calculating feature differences among different time phase images, and generating a change feature vector; and finally, constructing a dynamic change detection model based on the change feature vector, and generating a target area earth surface change detection result.

The invention relates to the technical field of electron microscopic imaging, in particular to a correction method and device for geometric distortion in scanning electron microscope measurement, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a scanning electron microscope image of a standard sample; calculating an orthogonal included angle parameter and a length-width ratio parameter of the scanning electron microscope image; iteratively adjusting the angle correction parameter and the gain correction parameter under a fixed scanning rotation angle until a preset local calibration constraint condition is met; verifying the orthogonal included angle parameter and the length-width ratio parameter under each scanning rotation angle; and when the orthogonality included angle parameter and the length-width ratio parameter do not meet a preset global stability constraint condition, adjusting a relative gain coefficient, and re-executing calibration verification under each scanning rotation angle until a full-angle correction requirement is met. By adopting the method, high precision and stability of imaging correction can be kept in a full-angle range.

The invention relates to the technical field of bill text recognition, and discloses a bill text recognition system and method based on deep learning. The method comprises the following steps: acquiring target bill original image data containing a multi-channel pixel matrix and spatial resolution information; based on a matching result of the bill edge features and a preset template, geometric distortion correction is carried out on the original image, and a corrected bill image is generated; inputting the corrected image into a pre-training text region detection network to obtain positioning information containing text line boundary coordinates and region confidence; text line image blocks are extracted according to the positioning information, character segmentation preprocessing is executed, and a character-level image sequence is generated; calling a deep character recognition model to classify the sequence character by character, and generating an initial text recognition result; semantic verification and error correction are performed on the initial result based on the bill type knowledge base, final structured text data are generated, and the processing requirements of bills of different types and qualities can be met.

The invention discloses a CNN-based high-performance lightweight optimization two-dimensional code recognition method, which belongs to the field of intelligent two-dimensional code recognition and comprises four steps of image preprocessing, lightweight CNN model construction and training, multi-scale feature extraction and decoding post-processing. According to the method, noise is suppressed through improved filtering, two-dimensional code edge details are reserved, and geometric distortion is corrected in combination with perspective transformation; the model parameter quantity and the calculation quantity are reduced through depth separable convolution, a channel attention module is embedded to strengthen key features, and a lightweight CNN model adaptive to the resource-constrained equipment is constructed; complete features are extracted through multi-scale feature fusion, and a decoding result is optimized in cooperation with RS code error correction and repeated recognition voting. Therefore, high-precision and real-time identification of the two-dimensional code in complex environments of uneven illumination, noise interference, distortion and the like is realized on a mobile terminal, an embedded device and the like, and the anti-interference capability and scene adaptability of the method are effectively improved.

The invention discloses a structural semantic guided bridge point cloud Gaussian splash modeling and rendering method, and belongs to the technical field of computer vision and bridge engineering digitization. The method comprises the steps of collecting a multi-view image of a bridge, and obtaining a sparse point cloud and a camera pose through a motion recovery structure technology; dense point clouds are generated, deep learning semantic segmentation is carried out, and key components such as piers, bridge floors and cables are identified; mapping the semantic tag back to the sparse point cloud; performing differential Gaussian element initialization on the sparse point cloud based on semantic tags, and endowing different parts with initial shapes conforming to geometric characteristics of the different parts; a geometric constraint loss function is introduced in the optimization process for joint optimization; and finally, outputting a Gaussian splash model of a high-fidelity and accurate geometric structure. According to the method, the problems of model redundancy and geometric distortion when a traditional Gaussian splashing technology is used for processing a bridge scene are solved, and lightweight and high-precision reconstruction and real-time rendering of the bridge model are realized.

The invention relates to the technical field of precision shell contour metering, and discloses an online metering method for a precision shell contour, and the method comprises the steps: obtaining an original sampling point cloud and a feature point group of a to-be-measured workpiece in a conveying state; resolving a real-time pose matrix of the to-be-measured workpiece relative to a preset measurement reference by using a spatial topology constraint relationship of the feature point groups; carrying out differential processing on a real-time pose matrix in the sampling sequence, and synthesizing an instantaneous motion vector corresponding to a sampling moment; in combination with a signal response time delay constant of the sampling system, phase compensation is carried out on a motion displacement deviator induced by signal conversion delay, and a corrected sampling point cloud is generated; according to the method, the geometric distortion of the point cloud generated by response delay of the sensor is solved, the problem of instantaneous drift of the measurement reference in an unsteady state conveying state is solved, and the contour reduction precision in a complex working condition is improved.

The invention discloses a hydraulic concrete structure underwater surface crack detection method, and relates to the technical field of image sensors, and the method comprises the steps: carrying out the transmissivity mapping and background radiation field separation of underwater light field characterization parameters, generating a target reflectivity image, carrying out the multispectral-polarization data fusion of the target reflectivity image, and generating an enhanced feature image; performing multi-scale convolution kernel space transformation and feature pyramid fusion on the enhanced feature image to generate a crack response diagram, and performing anisotropic non-maximum suppression and morphological skeleton refinement on the crack response diagram to generate crack detection data; and performing geometric topology joint optimization and parameter quantification on the crack detection data, and outputting a structured detection report. According to the invention, by constructing the light field transmission model and synchronously carrying out polarization and depolarization ratio combined noise filtering and geometric distortion correction, adaptive noise filtering is realized, and the measurement precision is improved.

The invention relates to the technical field of image processing, and discloses a fish school density determination method and device based on multi-range adaptive partition, computer equipment and a storage medium. According to the invention, the multi-modal perception model is used to replace the traditional mode of reading and marking the range of the sonar image depending on manual visual reading, and the polar coordinate-Cartesian coordinate mapping model is constructed based on the analyzed range parameters, so that the self-adaptive geometric partitioning and geometric distortion correction of the original sonar image are realized. A sonar image is decomposed into a plurality of microcosmic local block masks by generating local block masks, local effective fish school and local water volume are independently calculated in the local block masks, and finally the overall fish school density is determined. The method can be applied to the field of cultivation insurance insurance claim settlement business, and the capability of eliminating image distortion when an image system identifies and scans an image is improved.

The invention discloses an SAR geometric distortion identification method based on a local incidence angle and virtual topographic change, and the method comprises the steps: extracting a radar incidence angle and a satellite azimuth angle according to the SAR data of a target region, and calculating a radar sight line azimuth angle; dEM data of a target area are acquired, a slope angle and a slope direction are calculated, and a radar incident angle and a radar sight line azimuth angle are spatially interpolated into a DEM grid; calculating a local incident angle according to a radar incident angle, a radar sight azimuth angle, a slope angle and a slope direction in the DEM grid; determining four types of active geometric distortion information including perspective contraction, active overlay, resolution enhancement and active shadow according to the relation between the local incident angle and the radar incident angle; a critical point of the active geometric distortion area is searched, a virtual terrain is constructed pixel by pixel, a slope angle of the virtual terrain is calculated, and a position, equal to an incident slope angle, of the slope angle of the virtual terrain is searched through changes of the slope angle of the virtual terrain, so that a passive geometric distortion area is determined; and superposing the active geometric distortion and the passive geometric distortion to obtain final geometric distortion.

The invention relates to the technical field of physical property testing, and discloses a mobile phone appearance multi-angle automatic detection system and method based on a multi-face reflection structure, and the system comprises a panorama collection unit which obtains an original panorama image formed by a to-be-detected mobile phone through a multi-face panorama reflection mechanism; the reference data storage unit is used for storing standardized three-dimensional model data of the mobile phone to be tested; the central processing unit is used for establishing the three-dimensional model data as a space measurement reference, constructing an optical path mapping matrix according to the distribution parameters, mapping a surface grid of the three-dimensional model data to a camera coordinate system to determine geometric distortion sampling coordinates, extracting pixels to complete space topology reconstruction, and generating a standardized surface expansion view; according to the invention, nonlinear perspective distortion in reflection imaging is eliminated, tight alignment of pixel sites and physical topology is realized, and physical balance with objective scales is provided for quantitative analysis of flaws.

The invention discloses an image-text character recognition method and system based on AI intelligent examination, and relates to the field of character recognition. The method comprises the steps of calculating image features through image pixel intensity and local noise and generating a self-adaptive enhanced image, recognizing an effective region in combination with region weight, a local expansion index and geometric distortion, performing time sequence feature analysis on a candidate text region to generate a character sequence, correcting a character confidence value and extracting a key field, and obtaining a character sequence; and calculating non-linear verification to form an overall check value, generating a self-adaptive optimization signal according to the check value and a feedback error, and dynamically updating the recognition model and the field extraction model. Through adaptive image enhancement, candidate region recognition, character confidence correction, key field extraction and logic verification, high-precision image-text character recognition is realized, and model dynamic optimization and adaptive updating are supported.

The invention discloses a sonar image geometric correction method based on image optimization, and relates to the technical field of underwater sonar imaging, and the method comprises the steps: collecting a multi-source underwater observation data packet including sonar image original data, platform motion state data and underwater environment data, and carrying out the preprocessing; extracting sonar image original data from the multi-source underwater observation data packet, performing geometric coarse correction to obtain a pre-corrected sonar image, and performing motion compensation on the obtained pre-corrected sonar image to generate a motion corrected sonar image; constructing a joint optimization objective function, carrying out geometric fine tuning on the motion correction sonar image by adopting a sliding time window strategy, and outputting a geometric fine correction sonar image; geometric distortion caused by non-uniform motion of a platform, attitude disturbance and acoustic propagation distortion is effectively eliminated, inter-frame consistency and submarine topography continuity of sonar images are remarkably improved, and the method is suitable for high-precision operation scenes such as underwater surveying and mapping, target recognition and three-dimensional reconstruction.

The application discloses a cliff three-dimensional modeling method and system based on structure surface constraint, relates to the technical field of three-dimensional geographic information systems, collects cliff regional geological data including cliff multi-view images and cliff surface dense point cloud data, and constructs a geological prior knowledge base; image recognition technology is used to process the cliff multi-view images, combined with point cloud geometry analysis, to identify the spatial orientation of structure surfaces including rock layer interfaces and joint surfaces. The application effectively solves the geometric distortion problem of traditional digital elevation models in cliff regions by fusing geological prior knowledge, uses image recognition technology to extract the spatial orientation of rock layer interfaces and joint surfaces, and uses the spatial orientation as a hard constraint condition to construct a control triangular net framework, thereby avoiding the forced smoothing error of a regular grid DEM and the overhanging surface problem of an irregular triangular net, and through an adaptive triangular division algorithm, the rock layer structure, crack distribution and dangerous rock body shape of the cliff can be accurately restored.

A technology capable of suitably correcting a geometric distortion caused by a shape of a curved surface of a screen or the like and obtaining a suitable image easily viewable for a user is provided. A projector is an image display apparatus configured to project an image onto a screen and includes a projection lens arranged at a first position and a camera arranged at a second position. When the screen has a curved surface, a camera image obtained by capturing a first image projected on the screen by the camera is acquired, and a geometric transformation for correcting a geometric distortion caused by the curved surface seen from a first virtual viewpoint position (virtual viewpoint) is performed for an image to be displayed based on information of the camera image, the first position, and the second position, and a post-transformation image is projected onto the screen.

The invention provides a substation electrical test live detection system and method, and belongs to the technical field of power supply circuit monitoring, and the method comprises the steps: obtaining the comprehensive efficiency weight of each frame of dial region image according to a geometric distortion confidence index, an environmental interference penalty factor and the local stability condition of instrument reading values in an image sequence; performing weighted fusion on the instrument reading values of the continuous multi-frame images based on the comprehensive efficiency weight to obtain an instrument reading fusion value at the current moment; performing physical constraint verification on the instrument reading fusion value at the current moment according to the type of the to-be-detected equipment, and outputting a detection result of the electrical test of the transformer substation at the current moment; according to the method, environmental interference is effectively suppressed, and the accuracy and stability of live detection data of the transformer substation are remarkably improved.

The invention belongs to the technical field of computer vision and remote sensing image processing, and particularly discloses an arbitrary inclination angle unmanned aerial vehicle-satellite geographic positioning method and system, and the method comprises the steps: constructing an arbitrary inclination angle unmanned aerial vehicle-satellite geographic positioning model which is consistent with a view based on structure perception weighting; and inputting the unmanned aerial vehicle image or the satellite image into an arbitrary inclination angle unmanned aerial vehicle-satellite geographic positioning model which is consistent with the view based on structure perception weighting to carry out unmanned aerial vehicle-satellite image matching so as to realize arbitrary inclination angle unmanned aerial vehicle-satellite geographic positioning. According to the method, the feature expression of the model on the discriminative region is enhanced through the structure sensing context weighting module. According to the method, the problems of performance degradation and insufficient generalization ability when the problems of geometric distortion, shielding interference, feature redundancy and multi-view consistency in the arbitrary inclination angle unmanned aerial vehicle-satellite geographic positioning task in the prior art are solved, so that the arbitrary inclination angle unmanned aerial vehicle-satellite geographic positioning task can be better realized.

This invention discloses a dual-stream point cloud compression method and system based on semantic scene graphs, relating to the field of point cloud compression. It constructs a point cloud compression network comprising an encoder network and a decoder network: In the encoder network, semantic labels are mapped onto the input point cloud, and scene graphs and octree representations are constructed respectively. Semantic modulation parameters are generated based on the scene graph and encoded to obtain a semantic bitstream. The octree representation, under the influence of semantic modulation parameters, undergoes feature enhancement and attention probability prediction to obtain node occupancy probabilities, and a geometric bitstream is generated through arithmetic encoding. These two bitstreams serve as compressed data. In the decoder network, the bitstreams are decoded, and the octree structure and scene graph information are reconstructed. Node feature representations are recovered by combining the semantic modulation parameters. The reconstructed octree structure is mapped back to the point cloud space through spatial querying and indexing to reconstruct the point cloud. This invention solves the problem of local geometric distortion in low-bitrate octree point cloud compression, ensuring reconstruction quality while reducing the transmission bitrate.

The invention belongs to the technical field of power cable detection, and particularly relates to an image evaluation method for the twisting tightness of an overhead insulated cable, and the method comprises the steps: obtaining a cable detection image, carrying out the semantic segmentation and skeleton extraction, and unfolding a cable main body into a standardized ROI region through the polar coordinate transformation, so as to eliminate the geometric distortion; performing frequency domain transformation on the ROI region to obtain a power spectrum, and extracting multi-dimensional feature vectors including a twisting pitch frequency, an energy concentration ratio, a frequency spectrum symmetry unbalance degree and a harmonic broadening index; determining a defect type and calculating a confidence coefficient by calculating a weighted square distance between the feature vector and a prototype feature library; triggering an alarm or sending an adjusting instruction to the production line according to the detection result. According to the invention, cable jitter interference can be effectively overcome, and accurate on-line monitoring and closed-loop control of defects such as strand breakage and too loose twisting can be realized.

The invention discloses a remote sensing target detection model based on dynamic receptive field and multi-modal fusion. The remote sensing target detection model comprises the following steps: firstly, acquiring and preprocessing an optical remote sensing image and an SAR remote sensing image; then constructing a feature extraction network of the optical remote sensing image and the SAR remote sensing image, and obtaining feature output of the optical image and the SAR remote sensing image; modal-adaptive dynamic receptive field modules are embedded into the optical branch and the SAR branch respectively and are used for dynamically adjusting the size of a target receptive field according to target characteristics; then, through a cross-modal feature alignment and fusion module, the problem of geometric distortion is solved by utilizing deformable convolution, bidirectional alignment is performed on optical image features and SAR image features, and feature-level intelligent fusion is realized by adopting a space attention mechanism; according to the method, the complementary advantages of optical and SAR images are brought into full play, the size of a space receptive field is adaptively adjusted through a dynamic receptive field mechanism, and the calculation complexity is remarkably reduced while the detection precision is ensured.

The application discloses a bearing sealing structure internal defect nondestructive testing method and system, and relates to the technical field of nondestructive testing.The method comprises the following steps: before and after the start and end of a measurement task, calibrating a physical reference built in the system to calibrate and quantify system drift; during the scanning process, through a coaxial reverse differential measurement mechanism, real-time immunity to dynamic errors is generated to generate pretreated three-dimensional point cloud data flow; finally, through vector projection analysis of actual geometric distortion topological morphology and a predefined topological fingerprint representing a systematic aging mode, the diagnosis and compensation of the health state of the system itself are realized.Through an internal and full-cycle error defense system, the application can generate a three-dimensional model highly consistent with a physical entity, and the fidelity and long-term reliability of the detection result are improved.

The invention relates to the field of remote sensing image processing and terrain correction, and particularly discloses a radar geometric field-driven SAR distortion information adaptive neighborhood propagation identification method, which comprises the following steps of: S1, acquiring a Golden digital elevation model of a research area and a pixel-by-pixel ellipsoid incident angle graph generated by SAR satellite precision orbit parameters and imaging geometry, and carrying out geometric unified processing on the Golden digital elevation model and the pixel-by-pixel ellipsoid incident angle graph; s2, calculating a terrain parameter, an effective slope angle, an R geometric response field and an R field directivity gradient based on the unified raster data; s3, main distortion region classification is carried out according to the effective slope angle and the pixel-by-pixel ellipsoid incident angle; s4, defining an adjacent gradient and constructing a self-adaptive threshold value and a shadow self-adaptive threshold value; and S5, performing passive region propagation from the boundary of the main distortion region, and generating a final classification grid map after space overlapping correction. According to the method, the defect of blind propagation of the existing method is overcome, and high-precision automatic identification of SAR geometric distortion under complex terrains is realized.

The application discloses a kind of underwater concrete crack width automation measurement system and method based on double laser, it is related to underwater structure health monitoring and optical measurement technical field.The application includes: double laser projection module, for projecting a pair of circular laser points with fixed physical spacing to underwater concrete surface to be measured as physical scale reference;Image acquisition module, including underwater camera, for collecting image containing laser point and crack area;Data processing module, for performing underwater camera calibration, laser point detection, crack image segmentation and crack physical width calculation.The method corrects the image geometric distortion caused by "water-glass-air" refraction by establishing an accurate underwater multi-interface refraction imaging model;Nonlinear mapping relationship from image pixels to physical space is established using double laser points;Based on unsupervised crack segmentation model, crack area is automatically segmented, the physical width of each position of crack is calculated and width distribution cloud chart is generated.

The invention discloses a dynamic scene reconstruction method and system based on an event camera and a global shutter, and the method comprises the steps: S10, event collection: the event camera outputs an asynchronous event stream, and the data format is a sparse point set; s20, frame image collection: a global shutter camera collects a complete image at a fixed frame rate, and it is ensured that there is no geometric distortion; s30, timestamp alignment: forming a time sequence synchronous data pair by matching the timestamps of the event stream and the frame image; s40, feature fusion: motion compensation is carried out on a time domain, and texture and dynamic information fusion is carried out on a frequency domain; and S50, outputting a result: obtaining a fused dynamic scene reconstruction image. According to the invention, accurate timestamp alignment of the event stream and the global shutter image is realized; and the high dynamic range and the high-resolution texture are fused, so that the scene reconstruction quality is improved.