320 results about "Calibration Error" patented technology

The invention relates to the technical field of sensor calibration, and particularly discloses a high-precision automatic calibration system and method for a multispectral sensor. Comprising a multispectral signal acquisition module, a sensor response optimization module, a spectral feature matching module, a calibration parameter optimization module, an automatic calibration execution module, a calibration error evaluation module and a multi-node cooperative calibration module. The method comprises the following steps: acquiring an original spectral signal, and performing dynamic compensation to generate an optimized signal; extracting spectral feature points and matching the spectral feature points with a standard library to quantify feature deviation; fusing the environment data and the deviation value, and generating an optimal calibration parameter through optimization; real-time calibration is implemented based on parameters, and a cloud report is automatically generated; and constructing a calibration error evaluation model, evaluating the calibration error of the multispectral sensor, and dynamically adjusting a calibration strategy through cooperation of the distributed nodes and the central control unit. According to the invention, the automatic calibration process of the sensor is realized, and the calibration precision of the multispectral sensor in a complex environment is improved.

The invention relates to wide-range multi-component sensor calibration equipment and a wide-range multi-component sensor calibration method. The method comprises the following steps: firstly, checking equipment parts and setting a target temperature range and a compensation model of each temperature interval; then, dynamic temperature control is started, jig deformation is monitored in real time, a transient temperature gradient suppression technology is applied, multi-physical-quantity calibration is executed, and a compensation measurement value is output; then setting multi-environment coupling calibration test conditions to simulate actual working conditions, and monitoring and recording calibration data of each physical quantity; and finally, calculating a calibration error by combining a calibration result and a reference standard value. According to the process, by precisely setting parameters and combining dynamic control, real-time monitoring and multi-physical-quantity calibration, the transient temperature gradient influence is effectively inhibited, and the measurement precision of the sensor in a complex environment is ensured; the temperature control performance is improved through the micro soaking film and the intelligent temperature control strategy, the actual working condition is simulated through multi-environment coupling testing, the accuracy and reliability of the calibration result are remarkably improved through compensation and error calculation, and guarantee is provided for precise application of the sensor.

The invention particularly relates to a binocular vision-based three-dimensional reconstruction method, which comprises the following steps of: calibrating a binocular camera based on an improved Zhang Zhengyou calibration method to obtain internal and external parameters and a distortion coefficient of the camera; performing stereo correction on the image by using the internal and external parameters of the camera and the distortion coefficient obtained by calibration, so that the binocular image meets an epipolar constraint condition; a multi-strategy optimized semi-global stereo matching algorithm is adopted to process the image after stereo correction, and a disparity map is generated; based on the generated disparity map, generating a three-dimensional point cloud through a triangulation principle; carrying out anti-interference processing and registration optimization on the three-dimensional point cloud; and performing global splicing on the three-dimensional point clouds subjected to anti-interference processing and registration optimization based on a sequential registration error sharing strategy to complete three-dimensional reconstruction. According to the method, the key problems of large calibration error, weak texture matching failure, point cloud noise sensitivity and registration accumulative error in a traditional method are solved, and the reconstruction precision and stability are remarkably improved.

The invention discloses a three-dimensional shielding target tracking method based on multi-modal space-time interaction, and relates to the technical field of target tracking. The method comprises the following steps: acquiring a point cloud and an image and preprocessing to obtain global fusion features; obtaining an initial detection frame and region-of-interest features through region proposal network processing; projecting the non-empty voxel point cloud to the image features, and reconstructing shielded target features; convolution and neural network processing are utilized to obtain a refined detection frame; screening legal detection frames through distance calculation and legality judgment; the bipartite graph and the self-adaptive channel graph are adopted for convolution, and appearance correlation scores are calculated; and matching the detection frame and the trajectory based on a Hungary algorithm to realize whole-course tracking. The target identification accuracy and robustness are improved, the shielding problem is solved, the accuracy of the detection frame is ensured, the correlation accuracy is improved by using the bipartite graph and the adaptive convolution, the nodes are matched in combination with the geometric cost matrix, and whole-course tracking and error calibration are realized.

The invention relates to the technical field of electrical measurement, and discloses an electrical measurement instrument dynamic calibration error compensation method, which comprises the following steps: S1, multi-source data real-time acquisition: integrating a temperature sensor, a humidity sensor and an electromagnetic interference detection module in a measurement instrument, configuring voltage, current, frequency and other measurement channels, and carrying out multi-source data real-time acquisition at the frequency of not less than 100HZ; environmental parameters (temperature T, humidity H and electromagnetic interference intensity E) and measurement data (model characteristic parameters X1, X2,..., Xn such as voltage, current and frequency) are synchronously acquired. According to the electrical measuring instrument dynamic calibration error compensation method, through multi-source data real-time acquisition and composite error model construction, in combination with multiple linear regression and an improved BP neural network, environment and measurement signal changes are comprehensively captured, an error compensation value is accurately calculated, a measurement error can be effectively controlled within an extremely small range, and the error compensation accuracy is improved. Compared with a traditional method, the measurement precision is greatly improved.

The invention discloses a monocular and binocular cooperative localization and mapping method and device for underwater refraction compensation, and the method comprises the steps: extracting checkerboard angular points, compensating an underwater light path through employing a two-time vector correction model based on a Snell law, and precisely calibrating the internal and external parameters of a binocular camera. According to the method, an underwater real light path is effectively restored, and calibration errors caused by medium refraction are remarkably reduced. Then epipolar correction is performed on the corrected left and right images, so that the geometric consistency of stereo matching is ensured; and finally, monocular and binocular poses are fused, and seven-degree-of-freedom similarity transformation global alignment is applied, so that the problem of monocular scale uncertainty is effectively solved, and high-precision and high-robustness three-dimensional mapping is realized by means of binocular real scale introduction. Key links such as camera pre-calibration, refraction compensation, image correction, monocular tracking, binocular depth measurement, scale alignment and the like are connected in series to form an integrated process, so that the underwater vision positioning and mapping precision is improved.

The invention provides an intelligent electric meter calibration method and device, computer equipment and a storage medium, and the method comprises the steps: constructing a single meter-transformer area double-layer data system through a special transformer collection terminal, and collecting transformer area total intelligent electric meter data, user meter temperature and humidity, an electric energy observation value, transformer area line impedance and load unbalance degree data; constructing an energy conservation optimization function according to the user meter electric energy observation value and the total meter electric energy by combining the deviation with the observation value; determining line loss data according to total meter current and user temperature and humidity, determining power supply stability through total meter voltage, and determining optimization function constraint conditions according to corresponding data; and solving an optimal user table software compensation coefficient and a transformer area line loss correction coefficient by a generalized genetic algorithm, updating a humidity-error correlation coefficient and a line loss reference value by a feedback performance attenuation model to determine an adjusted single table calibration error range, and performing single table calibration by combining the user table temperature and humidity and an electric energy observation value. By adopting the scheme, the accuracy, the reliability and the environmental adaptability of the calibration of the intelligent electric meter are improved.

The invention provides a chip test calibration device and method, and relates to the technical field of chip test. The chip test calibration device comprises a base, a cooling air supply device, a control device, a chip and two groups of manipulators. According to the invention, the chip is heated by setting different temperature environments in stages, and the multi-point synchronous temperature measurement of each temperature stage is combined, so that the heat distribution on the surface of the chip can be comprehensively captured, and the calibration error caused by non-uniform temperature or a detection blind area is avoided; the chip is automatically circulated among the heating station, the detection station and the cooling station, synchronous processing and continuous detection of multiple chips are achieved, the single-batch detection period is greatly shortened, the surface of the chip is directly blown by directional airflow, an efficient air return channel is matched, the temperature of the chip can be rapidly reduced after detection is completed, idle waiting for station switching is reduced, and the overall production efficiency is improved.

The invention discloses an MEMS and MHD combined gyroscope low-high frequency composite calibration method, which comprises the following steps: synchronously acquiring vibration response data of a combined gyroscope sensor, separating low-high frequency signals, calculating low-frequency static error parameters and high-frequency dynamic error parameters, constructing a static orthogonal matrix based on a mounting angle error, and calibrating the low-high frequency composite of the MEMS and MHD combined gyroscope. The method comprises the following steps: generating a normalized error transfer coefficient by utilizing a high-frequency dynamic error parameter cross response ratio and a rejection ratio, generating a dynamic error matrix according to the normalized error transfer coefficient, establishing an expression containing error output of a sensor by utilizing the dynamic error matrix, and inversing the dynamic error matrix to obtain a dynamic error transfer matrix, and the static orthogonal matrix and the dynamic error transfer matrix are combined to construct a composite error compensation model, error parameters after dynamic compensation are evaluated, a residual feedback signal is generated in combination with an error evaluation result, calibration error parameters are iteratively corrected until convergence, and the calibration parameters are output.

The invention relates to the field of trajectory tracking, and discloses a gun trajectory tracking system and method fused with multi-source localization, the system comprises a satellite positioning module, an RFID node network, an inertial measurement unit (IMU) and a processing unit, the processing unit determines an open area, a semi-shielded area or a full-shielded area in real time through an environment classification sub-module, based on a dynamic weight distribution strategy, the fusion proportion of the satellite data, the RFID data and the I MU data is adjusted; synthesizing multi-source positioning data by adopting a weighted fusion algorithm, and outputting a real-time gun trajectory; and when the RFID node is located in a full-shielding area, the topological optimization calibration sub-module is combined with the RFID node topological relation to correct the accumulated error of the I MU, and weight distribution is dynamically fed back and adjusted based on the calibration error. According to the method, the problems of track interruption and drifting caused by failure of a single positioning source or inertial navigation accumulative errors in a complex shielding scene are solved, and high-robustness continuous tracking of gun tracks in multiple scenes such as cities, fields and indoors is realized.

The invention discloses an evidence deep learning method for double-layer dynamic uncertainty calibration based on a meta-strategy. A double-layer optimization architecture is adopted. An inner layer optimizes an evidence deep learning model to execute a pixel-level segmentation task and estimate uncertainty; and the outer layer optimizes a state-aware meta-policy network. The meta-policy network receives state information reflecting training dynamics in real time and generates key hyper-parameters used for configuring an inner-layer model loss function according to the dynamic state; and a multi-target reward signal is formed through performance in multiple aspects of prediction accuracy, calibration error, misclassification uncertainty and the like of a periodic evaluation model on a verification set. According to the method, hyperparameter dynamic adaptive adjustment is carried out by introducing a state-aware meta-strategy, the limitation that a traditional method depends on static setting is overcome, prediction precision and uncertainty calibration can be better balanced, and the reliability and generalization ability of a deep learning model in high-risk application scenes (such as medical image analysis) are remarkably improved.

The invention relates to the technical field of drainage plate positioning, and discloses a pile hole positioning method and system for a drainage plate pile pipe based on a multi-source sensor. Synchronous data acquisition of a multi-source heterogeneous sensor is achieved through a GNSS receiver and an IMU, a data basis is provided for subsequent fusion calculation, and then pile pipe root coordinates and pile pipe three-dimensional attitude angles are dynamically calculated through Kalman filtering fusion according to pile driver top coordinates and pile pipe motion data. Root posture resolving errors caused by static calibration errors and structural deformation in motion are avoided; then, the coordinates of the tip end of the pile pipe are dynamically calculated through coordinate rotation transformation, machine body motion data are introduced to compensate deviation calculation, and therefore the problem that in the prior art, due to the fact that the three-dimensional posture change of the pile pipe in the construction process is neglected, drainage plate pile hole positioning is not accurate enough is solved; and it is ensured that each pile hole can meet the design precision requirement, and the problem that the construction perpendicularity is uncontrollable is fundamentally solved.

The invention provides an error modeling method and system based on a CCD camera alignment assembly system, and relates to the technical field of high-precision electronic manufacturing assembly, and the method comprises the steps: obtaining internal parameters and external parameters of a CCD camera through calibration, building a camera and mechanical coordinate system conversion relation, and constructing a collaborative calibration error tree; controlling the motion platform to move, synchronously acquiring pose and environment temperature data, decomposing vibration and thermal deformation error components through time-frequency analysis, and superposing the vibration and thermal deformation error components into dynamic errors; extracting a positioning error based on the pose data, and constructing a mechanical-optical coupling error spectrum in combination with the distortion parameters; and finally, fusing the collaborative calibration error tree, the dynamic error and the coupling error atlas to generate a global error atlas, and completing alignment assembly system error modeling based on the CCD camera. According to the invention, the precision and applicability of error modeling are improved.

The invention relates to the technical field of data error correction, in particular to a calibration error correction method and system for a falling weight deflectometer, and the method comprises the steps: obtaining deflection values of a displacement sensor of the falling weight deflectometer to form a deflection program sequence, calculating deflection abnormal values of the deflection values, obtaining deflection rightward sequences of the deflection values, and calculating deflection error sequences of the deflection values; the method comprises the following steps: acquiring a deflection value of each deflection value, further acquiring a deflection continuation characteristic value of each deflection value, acquiring a load transfer weight of each deflection value through a correlation degree between local deflection sequences of deflection values of the same bit sequence in a program sequence during adjacent deflection, and acquiring a road looseness influence degree of each deflection value in combination with the deflection continuation characteristic value corresponding to each deflection value. Further acquiring deflection vibration characteristic values of the deflection values and smooth window scales of the deflection values, and smoothing the program columns during deflection so as to correct deflection data errors of the drop weight deflectometer. According to the application, the calibration error correction precision of the drop weight deflectometer can be improved.

The invention discloses a thunder-vision fusion multi-sensor collaborative sensing system along a railway and an installation and calibration method, and belongs to the technical field of intelligent traffic and environment sensing. Aiming at the problems of low target detection precision, poor multi-source data fusion timeliness, dependence on manpower on sensor installation and calibration and the like in a complex scene along a railway, the system provides an innovative architecture of'layered sensing-dynamic fusion-autonomous calibration '. The multi-modal sensor array of a laser radar, a millimeter-wave radar, a visible light camera and a thermal infrared imager is deployed; autonomous optimization of sensor pose parameters is realized by using track geometric constraint and a deep learning model; an edge computing node and a cloud collaboration platform are integrated, and real-time target tracking, intrusion early warning and equipment state diagnosis are supported. Experiments show that the target detection accuracy rate of the system is greater than or equal to 98.5%, the false alarm rate is less than or equal to 0.3% and the self-calibration error of sensor installation parameters is less than 0.05 degree in the scenes of rain and fog, night, high-speed movement and the like, which are improved by more than 40% compared with the traditional scheme.

The invention provides a water quality parameter inversion method and system based on spectrum correction and electronic equipment, and relates to the technical field of water environment remote sensing. According to the method, the water quality parameter and the water body reflectivity value of the surface feature spectrometer are measured synchronously when the remote sensing image passes through the scene, spectrum correction is carried out on the remote sensing image by using actually measured data as a reference, and the accuracy of water quality parameter inversion is improved. Firstly, water quality parameter and water body reflectivity value measurement of a surface feature spectrometer are synchronously carried out innovatively when a remote sensing image passes through a scene, so that the consistency of measured data and the remote sensing image in time and space is ensured, and a reliable basis is provided for subsequent spectrum correction and water quality parameter inversion modeling. Secondly, a spectrum correction model is established based on the synchronously acquired measured data, the spectrum of the remote sensing image can be adjusted according to the measured data, and the reflectivity deviation caused by atmospheric correction errors and sensor calibration errors is reduced, so that the accuracy of the spectrum data is improved.

The invention provides a calibration piece and a calibration method for a multi-port waveguide antenna test system, and a test method and a test system for a multi-port waveguide antenna, in the calibration piece, a positioning structure can ensure the repeatability of each connection, which is the premise of high-precision calibration, and the calibration accuracy is greatly improved. The load unit, the offset short circuit unit and the offset load unit are provided with a plurality of waveguide ports which are the same as those of a tested piece and can be directly butted, the internal structure of the waveguide port of the load unit is an antenna, electromagnetic wave energy can be radiated out instead of being absorbed internally, and high precision and stability of the load standard are guaranteed; according to the method, the reference surface can be determined at the interface of the tested piece, the calibration error model can be directly tested on the reference surface, the accuracy is good, and the solved calibration error model contains all time-varying characteristics of the whole test link during each calibration, so that the method can be used for frequently calibrating according to requirements, and the calibration accuracy is high. Measurement errors caused by temperature drift and link loss can be handled, and the efficiency is high.

According to the embodiment of the invention, leakage characteristic data of a plurality of sensors are collected in real time; performing filtering processing on the leakage characteristic data and then determining an area where a leakage source of the vacuum equipment is located; determining the confidence coefficient of the area where the leakage source is located, determining the confidence coefficient of the position of the leakage source based on the confidence coefficient of the area where the leakage source is located, and giving an alarm when the confidence coefficient of the position of the leakage source reaches a confidence threshold value; and injecting helium to the leakage source position of the vacuum equipment in a preset time period to perform calibration test, calculating a calibration error of the leakage source position of the vacuum equipment, and when the calibration error exceeds an error threshold value, recalculating the confidence coefficient of the leakage source position and resetting the confidence coefficient threshold value. The problems of high cost, low detection precision, slow response speed, dependence on artificial experience and incapability of real-time monitoring due to long-time use of a helium mass spectrum detection method when leakage positioning detection is performed on a leakage source of vacuum equipment in the prior art are solved.

A traffic scene external-field-free continuous calibration method comprises the steps that a traffic scene image is collected, geometric prior features in the image are extracted, and the geometric prior features are used for representing points existing on a distortion curve on the traffic scene image; an error function is defined, distortion parameters are optimized based on the error function, and the distortion parameters are used for representing geometric offset generated by lens distortion in the image and comprise radial distortion parameters and tangential distortion parameters; based on the optimal distortion parameter, performing orthodontic processing on the acquired image by adopting a nonlinear optimization algorithm; ground coplanar feature points are extracted from the orthodontic image, and camera external parameters including a rotation matrix and a translation vector are solved by combining corresponding world coordinate system positions of the ground coplanar feature points; calibration error distribution is evaluated based on simulation or actual scene data, distortion model parameters or optimization strategies are dynamically adjusted, and online parameter updating is triggered when it is detected that in-place posture deviation exceeds a threshold value. The method can simplify the calibration process and improve the calibration efficiency.

The invention discloses a multi-modal task model training method and device, a multi-modal task processing method and device, equipment and a computer program product, and the method comprises the steps: obtaining multi-modal sensor data, including laser point cloud data and image data; performing data enhancement processing on the multi-modal sensor data by using a data enhancement strategy; calibration data of the multi-modal sensors are obtained, training data of a multi-modal task model are constructed according to the enhanced multi-modal sensor data and the calibration data between the multi-modal sensors, and the calibration data comprise good calibration data and weak calibration data; and training the multi-modal task model by using the training data and a preset knowledge distillation strategy. According to the multi-modal model training method based on online knowledge distillation, on the premise of not depending on accurate calibration, the model can dynamically adapt to different calibration errors, the model training precision is maintained, and it is ensured that an automatic driving system can maintain the high-precision sensing ability in different environments.

The invention discloses an on-chip calibration device parasitic parameter determination and traceability method based on an inversion algorithm, and the method comprises the steps: determining eight error terms through TRL calibration which employs three calibration standard components: a straight-through calibration standard component, a reflection calibration standard component and a transmission line calibration standard component; the method comprises the following steps of: acquiring a relation between error terms in TRL calibration and SOLT calibration according to a signal flow graph, acquiring an expression for extracting a true value of a reflection coefficient of a calibration sheet through inversion according to a 12-item error correction model algorithm, substituting scattering parameters acquired by testing into an error term analytical solution of TRL calibration, solving a TRL calibration error term, and calculating the true value of the reflection coefficient of the calibration sheet according to the error term analytical solution of the TRL calibration. The reflection coefficient truth values of the open circuit and short circuit of the calibration piece and the load piece are extracted; according to the method, the extraction process of the parasitic parameters of the calibration piece is simplified, error sources introduced in the extraction process of the parasitic parameters of the calibration piece are reduced, and the extracted parasitic parameters of the calibration piece have a good effect.

The utility model discloses an on-line verification and calibration device of pressure detection equipment, which comprises a mounting seat communicated with a material conveying pipeline, a pressure detection pipeline communicated with the conveying pipeline is formed in the mounting seat, and a first valve is mounted at the end, communicated with the mounting seat, of the pressure detection pipeline. A pressure detection device is mounted at the other end of the pressure detection pipeline; the pressure detection pipeline is communicated with a pressure relief pipeline and a calibration pipeline; a pressure relief valve is installed on the pressure relief pipeline, and a second valve is installed at the communicating end of the calibration pipeline and the pressure detection pipeline. According to the utility model, on-line identification and calibration can be realized, and the second valve is arranged, so that calibration errors caused by pollution of calibration equipment due to pollution of the calibration pipeline by oil stains and the like can be effectively prevented.

The present invention discloses a method for calibrating the pose relationship between a robot and a turntable by using a laser tracker, including: Step 1: Divide the robot base into several regions and mark them; Step 2: Fix the laser tracker at a position between the robot base and the turntable. By placing several laser tracker accessories in several regions respectively, collect and record; Step 3: The robot is positioned on the robot base. Place several laser tracker accessories in the robot respectively, collect and record; Step 4: Place the laser tracker accessory at a fixed position on the turntable. By controlling the rotation of the turntable, rotate at intervals of 1 degree each time and record in sequence; Step 5: Change the fixed position of the laser tracker accessory. By controlling the rotation of the turntable, rotate at intervals of 1 degree each time and record in sequence. The present invention uses a laser tracker to calibrate the position relationship between the robot and the turntable, and maximally avoids the calibration errors caused by manual intervention.

The invention provides a calibration method, a calibration device, a tracking type scanning system and a storage medium, and the method comprises the steps: scanning a reference object on the calibration device through a scanner, and obtaining first scanning data corresponding to the reference object in a scanning coordinate system; acquiring the pose of the marker on the scanner and the pose of the calibration device by using a tracker; calibrating equipment parameters of the scanner based on the true value corresponding to the reference object and the first scanning data; based on the pose of the marker and the pose of the calibration device, converting the first scanning data to a tracking coordinate system of a tracker; and based on the true value corresponding to the reference object and the first scanning data under the tracking coordinate system, completing rigid body transformation relation calibration between the local coordinate system corresponding to the marker and the scanning coordinate system. Through the method, calibration errors can be reduced to improve measurement precision.

The invention discloses a pre-distortion calibration method and system based on a single chip microcomputer, a medium and a product, and relates to the field of radio frequency front ends. The method comprises the following steps: extracting an instantaneous amplitude value of an input signal sequence to obtain a first envelope to represent instantaneous nonlinearity; the periodic average power of the input signal is calculated and filtered through timer interruption, and a second envelope representing the heat effect is obtained. According to the method, the first envelope and the second envelope serve as double indexes, the preset two-dimensional lookup table is inquired, so that the pre-distortion calibration coefficient capable of being matched with the instantaneous nonlinearity and the thermal memory effect of the power amplifier at the same time is determined, and the input signal is calibrated. In addition, a feedback signal output by the power amplifier is obtained to calculate a distortion calibration error, and the two-dimensional lookup table is adaptively updated according to the error. According to the scheme, a composite calibration model is constructed on a low-cost single-chip microcomputer platform, and the accuracy and stability of nonlinear pre-distortion calibration of the power amplifier are guaranteed.

The invention relates to the field of data processing, and provides a panoramic look-around calibration method and electronic equipment. The method comprises the following steps: receiving an instruction of panoramic look-around calibration, obtaining panoramic look-around information, and carrying out panoramic look-around calibration on the panoramic look-around information by utilizing a corner calibration mode to obtain a first calibration result; in response to the fact that the first calibration result is a calibration error, a deep learning model is called, correct panoramic look-around calibration is completed on the panoramic look-around information through the deep learning model, and the deep learning model is a neural network model which is obtained through training in advance and can conduct panoramic look-around calibration. In this way, the deep learning model can be utilized to make up for the problem that a traditional angular point calibration mode is single in applicable scene, traditional angular point calibration and the deep learning model are matched with each other, then the panoramic look-around calibration conditions of various environment scenes can be met, the application range is wide, the accuracy is high, and the robustness of panoramic look-around calibration is improved.

The invention relates to a multi-sensor synchronous calibration jig and method. The precision, efficiency and stability of multi-sensor calibration can be improved. During specific operation, firstly, the multi-sensor array is accurately installed on the jig intelligent self-adaptive clamp and connected with an interface, and a system is electrified for self-inspection and uploads data information; then, starting a multi-dimensional state sensing network to collect environmental parameters, detecting the in-place state of the sensor and obtaining a self-inspection signal, and performing initial state evaluation; predicting the state of the actuating mechanism before action in state calibration and iteratively adjusting action parameters; finally, an effective parameter data point set and metadata thereof are input, traditional calibration parameter calculation is carried out, and state metadata serves as a covariable to be input into the error compensation model; according to the method, the accuracy, stability and reliability of sensor calibration are effectively improved through comprehensive evaluation of the initial state, dynamic adjustment of parameters, timely processing of abnormal data and the like, the calibration error is reduced, and the subsequent performance of the sensor is guaranteed.

The embodiment of the invention discloses a camera group calibration method and device, equipment and a medium, and relates to the technical field of camera calibration. The method comprises the following steps: acquiring a calibration object image of a to-be-calibrated object in a candidate camera coordinate system, and determining candidate calibration object coordinates in the candidate camera coordinate system according to the calibration object image; judging whether the candidate camera coordinate system is a reference camera coordinate system or not, and if not, converting the candidate calibration object coordinates under the candidate camera coordinate system into reference calibration object coordinates under the reference camera coordinate system according to a preset camera group transformation matrix; according to the reference calibration object coordinate and a preset calibration error function, determining corresponding calibration compensation data, and according to the reference calibration object coordinate and the calibration compensation data, determining a target calibration object coordinate of the object to be calibrated; wherein the calibration error function is determined based on sphere images of the calibration sphere at different acquisition positions in the view field overlapping area of the camera group and a preset sphere radius. And the space consistency and robustness of camera group calibration are improved.

The invention provides an automatic trapezoid correction method and system for a projector and a medium. The method comprises the following steps: acquiring attitude information of the projector and spatial information of the projector and a projection surface; under the condition that optical emission parameters of the projector are known, a projection geometric model is established based on the attitude information and the space information, and transformation parameters used for projector trapezoidal correction are generated accordingly. And controlling the projector to execute a trapezoidal correction action based on the transformation parameter. And controlling the projector to project a preset calibration pattern, acquiring an image of the calibration pattern by adopting the image acquisition device after the trapezoidal correction action is executed, extracting image features, and acquiring geometric parameters corresponding to the actual projection picture based on the image features. And determining a calibration error based on the difference between the geometric parameter and the transformation parameter, and generating a compensation parameter according to the calibration error. And writing the compensation parameter into the projector so as to compensate the transformation parameter when the projector performs trapezoidal correction. Through the steps, the projector generates a corresponding correction coefficient.

The invention provides a three-dimensional head shadow measurement method and device, a medium and equipment, and the method comprises the steps: obtaining a craniofacial three-dimensional image of a subject, carrying out the preprocessing, inputting the preprocessed three-dimensional image into a neural network model which is trained in advance, outputting the key information of the craniofacial in the three-dimensional image, and obtaining the three-dimensional head shadow according to the key information. And automatically calculating measurement parameters according to a predefined measurement rule, and displaying a measurement result obtained by calculation. According to the method, key points are automatically positioned through three-dimensional deep learning, a complex craniofacial structure is finely recognized, and calibration errors are reduced; the system automatically completes data preprocessing, key point detection and parameter calculation, manual calibration dependence is reduced, subjective errors and operation complexity are reduced, clinical efficiency is improved, a result is displayed through a three-dimensional visual interface, and doctors can rapidly understand and check; interactive punctuation fine tuning is provided, and efficiency and flexibility are both considered; the model can be trained based on various craniofacial data, is compatible with a common measurement system, and can add measurement items or analysis dimensions as required.