300 results about "Digital image correlation" patented technology

The invention relates to a temperature field and deformation field synchronous measurement system and method based on laser speckle DIC and temperature field algorithm temperature measurement. The system comprises a 3CCD color camera, a lens, a three-band filter, a monochromatic light source, a mechanical support adjusting platform, a triangular support and a computer. A test piece is actively illuminated by blue laser, and a color image containing three channel sub-images of red, green and blue on the surface of a high-temperature sample before and after deformation is acquired by a 3CCD color camera. A temperature field algorithm is used for analyzing sub-images of the R channel and the G channel after deformation to calculate a temperature field of the surface of the test piece, and a digital image correlation method is used for analyzing sub-images of the B channel before and after deformation to obtain a strain field of the surface of the test piece, so that synchronous measurement of the temperature field and the deformation field of the surface of the test piece is realized. The principle is simple, the system structure is compact, temperature field measurement is not interfered by speckles, and non-contact, high-precision and synchronous measurement of a material deformation field and a temperature field under the action of force and thermal loads can be achieved.

The invention relates to a deformation online measurement and control method for multi-robot collaborative assembly. The method comprises the steps that multi-view surface images of workpieces in the assembly process are collected in real time through distributed robots and cameras arranged at the tail ends of the distributed robots; each view angle surface image is input into a deep learning model trained based on a digital image related technology, the optimal pixel displacement corresponding to each view angle surface image is output, and the optimal pixel displacement corresponding to each view angle surface image is converted into a spatial displacement label; the deep learning model takes an encoder-decoder as a trunk network; fusing the spatial displacement labels corresponding to the view angle surface images to obtain a fused displacement field; based on the fusion displacement field and the nominal path planning point, the tail end pose of the distributed robot is determined; the assembly error is calculated based on the reference target positioning and the tail end pose, and the PID controller adjusts the joint space of the distributed robot based on the assembly error. According to the method, the calculation overhead is remarkably reduced, and the measurement precision and robustness are improved.

The invention relates to the technical field of digital image correlation DIC measurement, in particular to a DIC displacement field dynamic correction method and system based on a lightweight convolutional network, and the method comprises the steps: 1, collecting a structure surface image sequence, and carrying out the preprocessing; 2, calculating an initial displacement field of the preprocessed image sequence through a traditional DIC algorithm, extracting key feature information, and taking the key feature information as input data for dynamic correction; 3, constructing a lightweight convolutional neural network model, transmitting the extracted input data to the lightweight convolutional neural network model for recognition, designing a mixed loss function for training, and learning a mapping relation between an initial displacement field and a real displacement field; and 4, correcting the initial displacement field through the trained lightweight convolutional neural network model, and directly outputting corrected high-precision displacement field data. By using the function fitting capability of the neural network, high-precision displacement field correction is realized under limited computing resources, and the measurement precision is improved.

The invention discloses an in-situ characterization method for fracture toughness of a high-temperature interface of a thermal barrier coating, which comprises the following steps: taking a metal test piece with the thermal barrier coating as a sample to be tested, spraying NiAl on the surface of a ceramic layer in the thermal barrier coating to serve as a buffer layer, then spraying a reinforcing layer, and forming a mixed reinforcing layer with the buffer layer; a three-point bending experiment is carried out on a to-be-tested sample at 1000-1400 DEG C by using a three-point bending device with a digital image correlation method in-situ observation function, and the three-point bending process is recorded in real time. A strain cloud picture of an observation area is obtained through a digital image correlation method, parameters are obtained from the strain cloud picture, and the interface critical energy release rate and the stress intensity factor of a three-point bending experiment are calculated and are both in direct proportion to the high-temperature interface fracture toughness of the thermal barrier coating. Therefore, in-situ characterization of the interface fracture toughness of the thermal barrier coating can be realized in a high-temperature environment of real service of the thermal barrier coating, test data can be accurately acquired in real time, and the test period is short.

The invention relates to a slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity. The method comprises the following steps: firstly, carrying out geological survey on a site slope to obtain variability parameters of a slope soil body; then, a slope finite element model is constructed on the basis of slope geometric parameters and the variability parameters, and the displacement states of the slope under different parameter conditions are determined on the basis of the slope finite element model; grading the slope sensitive area based on the displacement state; obtaining displacement data based on the grade of the slope sensitive area, and determining a displacement correlation coefficient and a spatial correlation coefficient based on the displacement data by adopting a Pearson's correlation coefficient and a digital image correlation technology; and finally, determining a slope high-correlation area based on the displacement correlation coefficient and the spatial correlation coefficient, and setting a monitoring point position. The scientificity and accuracy of slope monitoring point arrangement are improved, and the method has certain significance for predicting and preventing slope disasters.

The invention discloses a thermal shock failure prediction and laser repair device for a thermal barrier coating and an application method. The device comprises a multi-physics field coupling simulation analysis module, a thermal shock experiment loading module, an online damage monitoring and failure diagnosis module, a robot laser repair execution module and a central control and data processing core unit. A thermal-mechanical-chemical coupling model is established to predict a coating failure behavior, a high-frequency induction heating and gas quenching system is adopted to simulate an extreme thermal shock environment, a multi-band thermal infrared imager, an ultrasonic probe and a digital image related system are integrated to monitor damage evolution in real time, and in-situ repair of a damaged area is realized based on a six-degree-of-freedom industrial robot. According to the method, the whole-process closed-loop control from failure prediction to repair regeneration is realized, the problems of single function and lack of multi-field coupling analysis and real-time repair capability in the prior art are solved, and the service reliability evaluation precision and the remanufacturing efficiency of the thermal barrier coating are remarkably improved.

Optical inspection systems, methods, and media are configured to perform and / or implement receiving a time series of 3D point cloud images of a target object from an imaging system, wherein the 3D point cloud images include first directional position data, second directional position data, and change in first directional velocity data; applying a self-supervised machine learning model to the time series of 3D point cloud images, wherein the trained machine learning model has been trained on a plurality of training point cloud images to which simulated cracks have been added; and identifying a mechanical failure in the target object based on an output of the machine learning model.

The invention discloses a method and a system for rapidly measuring cable force of a stay cable based on a digital image correlation method. The method comprises the following steps: acquiring video data of the stay cable; a line segment measurement calibration method is adopted, stay cable texture points serve as feature points, and a feature point selection mechanism is established; establishing a pixel-physical space coordinate mapping model based on a redundant observation method; model parameters are calculated through a collaborative calibration method; performing displacement measurement based on a digital image correlation method; establishing a double-layer feature extraction mechanism to complete local and global feature extraction; weight distribution of the local features and the global features is dynamically adjusted through a weight adaptive fusion strategy; and finally, establishing a double-layer tracking and failure recovery mechanism to complete hierarchical collaboration of coarse-grained global tracking and fine-grained local tracking. And carrying out fast Fourier transform on the dynamic displacement measurement result, and calculating to obtain the cable force. And reliable technical support is provided for non-contact accurate calculation and measurement of the cable force.

An exemplary system and method that employ inverse-problem analysis that can determine spatially-varying mechanical parameters in a spatially-varying field of a heterogeneous material. Mathematically, the computation simultaneously poses the inversion program and an image registration problem in a continuum limit function space setting to derive a discretization dimension-independent algorithm for the robust inference of heterogeneous material properties. The algorithm can operate using two or more images of a speckled pattern or other non-uniform patterns applied to, or observable of, the surface of the material in a first state and a second state different from the first state. The difference can be used to assess, via a Newtonian-based operator, the infinite-dimensional spatial fields as state variables that are regularized via a regularization model to constrain the inherent ill-posed nature of inverse problems.

The invention belongs to the field of material mechanical property testing and strain measurement, and particularly relates to a local strain measurement method and system based on a four-point bending and digital image correlation (DIC) technology, and the method specifically comprises the following steps: cutting to prepare a sample, carrying out surface electrolytic polishing, preparing speckles on the surface of the sample to be tested, and carrying out a four-point bending experiment; speckle images of the lower surface of the sample are collected, displacement correction is conducted in combination with bending deflection calculation, the bending load and the strain of the upper surface of the sample are obtained in real time, the stress-strain relation and local strain distribution of the lower surface of the sample are obtained through calculation, and finally deformation and failure mechanism analysis is conducted in combination with surface deformation and crack morphology. The method provided by the invention combines four-point bending and DIC plane strain measurement technologies, can realize stretching, compression and dynamic fatigue loading on the surface of the sample, is suitable for performance test and failure analysis of tension, compression and fatigue deformation of brittle metal materials such as intermetallic compounds and amorphous materials, and has the characteristics of convenience, rapidness and low cost.

The invention discloses a cylindrical roller trajectory tracking method and device, and the cylindrical roller trajectory tracking method comprises the following steps: obtaining an actual measurement displacement sequence of a key point on the end surface of a roller under a global coordinate system based on a multi-reference matching digital image correlation method; drawing a key point displacement curve about key points and time according to the actually measured displacement sequence; judging the motion state of a roller according to the shape characteristics of the key point displacement curve, dividing a smooth part in the key point displacement curve into a rolling area, and dividing a part with jumping or sharp points into a sliding area; calculating the actual revolution angular velocity and the actual rotation angular velocity of the roller based on the actually measured displacement sequence corresponding to the sliding area; according to the method and the device, the image matching precision can be improved, meanwhile, the rolling and sliding components of the roller are accurately distinguished, the precision of the track of the cylindrical roller is improved, and high-precision and visual experimental data are provided for existing dynamics and tribology models.

The invention discloses an integrated fatigue small crack initiation in-situ monitoring device and a detection method, and belongs to the technical field of material mechanical property testing. Comprising a fatigue test system, a digital image correlation test system and an acoustic emission test system, the fatigue test system is used for detecting the fatigue performance of a sample under loading force in situ and comprises a console and an in-situ fatigue testing machine, and two tensile fixtures are symmetrically arranged on the in-situ fatigue testing machine; the digital image correlation test system is used for in-situ detection of time and space trajectories of crack initiation and expansion under fatigue of a sample, and comprises a camera, a bracket and an image acquisition card; the acoustic emission test system is used for in-situ detection of an accumulation process of internal damage of a sample under fatigue, and comprises an acoustic emission sensor, a preamplifier and an acoustic emission acquisition card. By simultaneously capturing and recording the mechanical response, the optical image and the acoustic emission signal of the sample in the fatigue test period, real-time in-situ monitoring of multi-source information such as mechanical, optical and acoustic emission is realized.

The invention discloses a deep learning-based machining surface plastic deformation measurement method and system, and belongs to the technical field of metal cutting machining. According to the method, a parallel encoder-decoder structure is adopted, and spatial and temporal features and spatial features of a deformed image pair are fully extracted through multi-layer multi-scale convolution contraction and expansion and are used for predicting a deformation field. A double-attention mechanism module is introduced, position and channel characteristics are combined more effectively, efficient and high-precision measurement of a material deformation field is achieved, and a traditional digital image correlation method can be effectively replaced. According to the method, a Boolean model is used for generating an initial speckle capable of approaching a real environment, a simulated deformation image pair is obtained through image distortion, and the availability of a virtual data set is evaluated by using an autocorrelation full width at half maximum, a system error, a random error and an average gray scale gradient; a large number of high-quality data sets for deep learning training are efficiently provided, so that the model is free from limitation of a real data set, and the generalization of the model is improved.

The invention relates to a slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity. The method comprises the following steps: firstly, carrying out geological survey on a site slope to obtain variability parameters of a slope soil body; then, a slope finite element model is constructed on the basis of slope geometric parameters and the variability parameters, and the displacement states of the slope under different parameter conditions are determined on the basis of the slope finite element model; grading the slope sensitive area based on the displacement state; obtaining displacement data based on the grade of the slope sensitive area, and determining a displacement correlation coefficient and a spatial correlation coefficient based on the displacement data by adopting a Pearson's correlation coefficient and a digital image correlation technology; and finally, determining a slope high-correlation area based on the displacement correlation coefficient and the spatial correlation coefficient, and setting a monitoring point position. The scientificity and accuracy of slope monitoring point arrangement are improved, and the method has certain significance for predicting and preventing slope disasters.

The invention relates to the technical field of structural design and optimization, and particularly discloses a structural error tolerance model construction method for compartment connection positioning, which comprises the following steps of: obtaining real point cloud data of a bolt connection pair through three-dimensional laser scanning, and realizing parameterized digital geometric modeling in combination with a deep neural network; material attributes are given, and self-adaptive grid division is carried out; generating a dynamic load sequence based on a vehicle driving condition, applying the dynamic load sequence to the model, and extracting full-field displacement vector data by using a digital image correlation technology; further identifying a micro-slip area of the contact interface, and calculating an energy dissipation index of the micro-slip area; a quantitative mapping relation between energy dissipation and fatigue life is established through experimental calibration, key design parameters are iteratively optimized by adopting a pattern search method with the target life as a constraint, and finally a connection structure scheme meeting the durability requirement is output.

The invention discloses a fragile material crack detection and damage evaluation method based on box dimension. Change information of a material surface displacement field is calculated by adopting a DIC technology (a digital image correlation technology), a main strain graph is obtained, then the box dimension of the material surface main strain field is calculated by combining a fractal theory, and the damage degree of the material is quantified based on change development of the box dimension. According to the invention, the development process of the cracks of the material test piece from no to penetrating through the test piece can be observed, the crack growth path can be obtained, the crack development can be quantified through the specific box dimension, the damage degree of the structure can be evaluated, and the early warning of the large crack propagation behavior is realized. The method has important practical engineering significance for health detection and risk assessment of fragile material structures such as steel fiber reinforced concrete.

The invention discloses a wide-temperature-range three-dimensional pressure field measurement method in an extreme wind tunnel environment, and the method achieves the high-precision reconstruction of a three-dimensional pressure field in a whole temperature range from low temperature to high temperature through the fusion of a digital image correlation (DIC) technology and a multi-mode sensing technology, specifically, in a low-temperature environment, pressure-sensitive paint and temperature-sensitive paint are used for joint calibration, and the measurement precision of a wide-temperature-range three-dimensional pressure field is improved. A laser light source and double cameras are synchronously triggered through a binocular vision system, fluorescence lifetime signals are captured, temperature interference is eliminated through a double-gating gain function ratio, a three-dimensional shape is reconstructed in combination with a DIC technology, and non-contact pixel-level pressure field mapping is achieved. In a high-temperature environment, pressure measuring point layout is optimized through CFD simulation, discrete pressure data are acquired by adopting a high-temperature-resistant pressure sensor array, sensor space coordinates are acquired in combination with a binocular DIC system, a two-dimensional pressure field is reconstructed by utilizing a Kriging interpolation algorithm, and a three-dimensional pressure field is obtained through pixel-level mapping and three-dimensional point cloud fusion. And a high-resolution three-dimensional pressure field in a high-temperature environment is generated.

The invention provides a method for solving transgranular or intergranular failure of a rock material numerical model based on two-dimensional digital image correlation, and belongs to the field of rock mechanics and geotechnical engineering. The method comprises the following steps: firstly, collecting surface structure characteristics of a rock material, and acquiring geometric characteristics of the surface of the rock material through a scanning technology; the microscopic structure of the rock material is modeled, a Voronoi division method is adopted to divide a numerical model, and in combination with surface feature data, geometric features of the rock material are precisely restored, model parameters are adjusted, and guarantee is provided for subsequent loading operation. And secondly, carrying out loading operation on the established model, simulating deformation and damage processes of the rock under different stress conditions, and monitoring a displacement field on the surface of the model in the loading process to obtain high-precision displacement data. And finally, judging the type of the destruction mode according to the displacement data. According to the method, the type of the rock numerical simulation microscopic crack can be judged, the method is convenient, and the result is accurate.

The invention provides a ground-air combined road slope collapse measurement system and method, and relates to the field of road slope inspection. According to the system, all-weather non-contact acquisition of millimeter-level displacement data of side slope key points is realized through a scientific-level CMOS camera of the ground detection module and a digital image correlation algorithm, and a high and steep side slope is covered; the aerial detection module carries a synthetic aperture radar and a multispectral sensor through an unmanned aerial vehicle to obtain a slope overall three-dimensional deformation field and soil humidity data. Carrying out high-precision space-time registration on the data layer by adopting a dual-function mark point and a least square method; calibrating aerial deformation data on the feature layer through ground displacement data to realize association of macroscopic and microscopic features; and constructing an improved U-Net network and a hybrid prediction model in the model layer to realize accurate tracking of the crack and prediction of the collapse probability. Visual display is carried out through a digital twin platform, and graded early warning linked with a traffic management system is triggered. According to the invention, the limitation of a single detection mode is overcome, and accurate perception, intelligent prediction and dynamic response of road slope safety are realized.

The invention belongs to the technical field of tailing strain field evolution prediction, and discloses a full-tailing cemented filling body strain field evolution and crack prediction method, which comprises the following steps: preparing a full-tailing cemented filling body CPB sample, and carrying out an expansion degree test in the preparation process of the CPB sample; evaluating the uniaxial compressive strength of the CPB sample, and monitoring the surface change of the sample in the testing process through a digital image related acquisition technology; establishing a computer visual identification model based on an HSV color mode, and analyzing the area proportion and distribution condition of strain regions of different degrees to realize comprehensive analysis of the image; and determining an early warning signal of crack prediction according to an analysis result of the change condition of the strain field. According to the strain field evolution and crack prediction method for the full-tailing cemented filling body, quantitative analysis of strain evolution of the filling body is achieved, prediction and early warning are provided for the damage risk of the filling body, and the method has important significance for ensuring the safety and efficiency of mine operation.

The invention belongs to the technical field of three-dimensional vision measurement, and relates to a deformation measurement method and system of a composite vision measurement unit. The internal and external parameters of the binocular measurement unit are obtained according to the image of the thrust vectoring nozzle target area calibration plate; obtaining coordinates of all binocular measurement units in the same reference coordinate system according to the internal and external parameters and the images of the thrust vectoring nozzles with the first coded mark points, and obtaining coordinates of all binocular measurement units in a unified coordinate system; and acquiring full-field deformation of all target areas of the thrust vectoring nozzle according to the speckle images of the target areas of the thrust vectoring nozzle in the working state and the coordinates of the unified coordinate system of all binocular measurement units. According to the invention, a digital image correlation method and a close-range photogrammetry technology are utilized to realize coordinate system unification and full-field deformation measurement. Compared with a traditional method, the measurement times and data processing steps are reduced, and the measurement efficiency is improved.

The application discloses a kind of based on transparent soil and pile-soil interface micro evolution measurement method of multi-source sensing, it is related to geotechnical engineering test field.The present application includes the following steps: constructing transparent soil model, pile body pre-embedded sensor arrangement, multi-source sensor calibration and parameter optimization, load application and multi-source data acquisition, improved DIC algorithm's pile-soil interface micro displacement calculation, pile-soil interface pore and particle density calculation, multi-source sensing data's heterogeneous fusion, pile-soil interface micro evolution characteristic quantitative inversion and visualization, test end and data verification.The present application simulates real soil environment by constructing transparent soil model, in combination with image recognition, digital image correlation, fiber-optic sensing and resistance sensor and other multi-source sensing technology, the micro deformation, stress distribution and failure evolution process of pile-soil interface in loading process are monitored and analyzed in real time with high precision, realize the high-precision, full-process, visual measurement of pile-soil interface micro evolution, improve measurement precision.

The invention discloses a multifunctional asphalt mixture rut test method which is implemented by using a device integrating loading and measurement, test piece supporting and temperature control drainage, environment simulation and optical measurement and a central control and data acquisition system. A test piece is arranged in the test piece groove, temperature control is realized through circulating a heat-conducting medium in the temperature-conducting cavity, different gradients are formed through the hoisting mechanism and the angle adjusting assembly, the test wheel is driven to perform one-way circulating loading along a closed track and snub at a preset moment, and rolling and sliding friction forces are synchronously collected; in the test process, rainfall is simulated by adopting the watering assembly, incoming water is recycled by the drainage tank, speckle images are acquired by the high-frequency industrial camera, and surface full-field displacement and strain are obtained through digital image correlation analysis. Through time registration and fusion of temperature, gradient, wheel speed, rainfall and friction-deformation data, rut deformation, skid resistance and apparent structure evolution laws of the asphalt mixture under multi-climate and multi-gradient conditions are obtained.

The invention belongs to the field of metal high-temperature performance testing, and relates to a metal material high-temperature tensile testing device and testing method. The device comprises a synchronous control box, the synchronous control box is respectively connected with the electromagnetic valve, the universal testing machine, the infrared thermal imager and the three-dimensional digital image related equipment; the constant current source is respectively connected with the upper and lower parts of the high-temperature clamp; the high-temperature clamp is mounted in the universal testing machine and comprises an upper clamping part and a lower clamping part which are used for clamping a metal sample; the cooling pipeline is connected with the air compressor, and the electromagnetic valve is mounted on the cooling pipeline connected with the air compressor; during testing, speckles are sprayed on one surface, facing three-dimensional digital image related equipment, of a metal sample, and the metal sample is clamped in the middle of a high-temperature clamp; one end of the thermocouple is welded on the surface without speckles of the metal sample, and the other end of the thermocouple is connected to the constant current source and is connected to the synchronous control box. The problems that when a high-temperature extensometer is used for measuring sample deformation, the measuring range is insufficient, damage is prone to occurring, and additional interference is introduced are solved.

Methods, systems, and non-transitory computer readable storage media are disclosed for generating neural network based perceptual artifact segmentations in synthetic digital image content. The disclosed system utilizing neural networks to detect perceptual artifacts in digital images in connection with generating or modifying digital images. The disclosed system determines a digital image including one or more synthetically modified portions. The disclosed system utilizes an artifact segmentation machine-learning model to detect perceptual artifacts in the synthetically modified portion(s). The artifact segmentation machine-learning model is trained to detect perceptual artifacts based on labeled artifact regions of synthetic training digital images. Additionally, the disclosed system utilizes the artifact segmentation machine-learning model in an iterative inpainting process. The disclosed system utilizes one or more digital image inpainting models to inpaint in a digital image. The disclosed system utilizes the artifact segmentation machine-learning model detect perceptual artifacts in the inpainted portions for additional inpainting iterations.

The invention discloses a digital image correlation method based on a deep learning image matching algorithm, and belongs to the field of deformation measurement. The implementation method comprises the steps that speckle images before and after sample deformation are collected, the speckle image before deformation serves as a reference image, the speckle image after deformation serves as a target image, and a calculation area and calculation points are arranged in the reference image; using an image feature extraction network based on a deep convolutional neural network to extract key features of the reference image and the target image; matching the features by using an image feature matching network based on Transform to form feature point pairs; removing wrongly matched feature point pairs, and calculating a deformation initial value by using affine transformation; based on the deformation initial value and an IC-GN algorithm, a final high-precision deformation field is calculated in a non-linear iteration mode, and digital image correlation is achieved based on a deep learning image matching algorithm. According to the invention, matching of speckle features is realized based on a deep learning image matching algorithm, and deformation measurement of a low-quality speckle image can be realized.

A system and method for analyzing mechanical properties of biological tissues combines light-sheet fluorescence microscopy with mechanical perturbation capabilities. The system integrates a MEMS-based compression device, high-speed light-sheet scanner, and digital image correlation analysis to characterize viscoelastic properties of three-dimensional cellular structures. The system enables quantification of spatially-resolved mechanical characteristics and cellular responses through volumetric strain analysis, correlating mechanical properties with fluorescence-based cellular markers. Implementation with maternal-fetal interface models demonstrates capabilities in analyzing tissue mechanics during cellular invasion processes. The system provides applications in studying mechanical interactions in biological processes including embryonic development, cancer metastasis, and wound healing. The disclosed methodology enables unprecedented analysis of mechanical property dynamics at cellular interfaces in three-dimensional tissue environments.

The invention discloses an ancient building durability monitoring system based on micro-deformation monitoring and machine learning, which is used for predicting the water content of a wood structure of an ancient building through monitoring local wood grain micro-deformation of the wood structure of the ancient building and machine learning based on a multi-Fick coupling unsteady state water transfer model and Bayesian inversion, and further monitoring the durability of the ancient building. According to the method, digital image correlation is used, the sub-pixel precision is considered, and the machine learning model is corrected through temperature and humidity, so that the durability of the ancient building is monitored more accurately.

The invention discloses a granular material micromechanics test platform and method based on an optical measurement technology, the test platform is provided with a polarized light source, a polarizer, a biaxial loading device, an LED light source, an analyzer and an image acquisition system from left to right in sequence, and the optical platform is also provided with a control system. And the control system is electrically connected with the biaxial loading device. The granular material micromechanics test platform is based on an optical measurement technology, shearing of a two-dimensional dense particle system is achieved through movement of the adjustable biaxial loading device, switching between a polarized light field and an unpolarized light field can be achieved by rotating the polarization analyzer, so that test images under different light fields are obtained, and the test precision is improved. A photoelastic technology and a digital image related technology are combined to carry out contact force and dynamics analysis; according to the granular material micromechanics test platform, contact force and dynamic information of the granular material can be visually and synchronously obtained through a high-precision and high-acquisition-frequency image acquisition technology without contact.

The invention provides a large-view-field high-precision monitoring method and system, and relates to the technical field of real-time monitoring. The detection method combines a machine vision technology and a DIC technology based on deep learning to optimize a monitoring process, can perform high-precision deformation monitoring on multiple points of a large view field, and is suitable for deformation monitoring of reservoir dam abutment rock mass; before detection, a convolutional neural network is used to carry out defogging operation and intelligent analysis on a surface image of a to-be-detected area, a key target area is automatically identified, and errors caused by manual intervention are effectively avoided; after the target area is determined, displacement field distribution of the target area is calculated by adopting a digital image correlation algorithm based on deep learning, and deformation measurement with sub-pixel-level precision is realized; through fusion of deep learning and a traditional image processing technology, the influence of manual parameter setting is reduced, the problem of decorrelation during matching is solved, and the precision and reliability of displacement calculation are effectively improved.