180 results about "Image calibration" patented technology

The invention belongs to the technical field of space remote sensing surveying and mapping, and particularly relates to a monitoring system and method for a front and rear view visual axis included angle and absolute direction of a surveying and mapping camera. The method comprises the following steps: S1, through side sway of a satellite platform, taking a time for sky imaging calibration as a reference time; s2, calculating a rotation matrix from a front and rear view ground camera measurement coordinate system to a star camera coordinate system at a reference moment; s3, calculating respective thermal deformation matrixes of the star camera coordinate system, the front-view camera measurement coordinate system and the rear-view camera measurement coordinate system; s4, obtaining a real-time rotation matrix from the measurement coordinate system of the front-view camera to the coordinate system of the star camera and a real-time rotation matrix from the measurement coordinate system of the rear-view camera to the coordinate system of the star camera at the measurement moment; and S5, carrying out arc cosine calculation on the two absolute pointing vectors to obtain a visual axis included angle between the front-view camera and the rear-view camera at the measurement moment. According to the invention, while the geometric accuracy of the surveying and mapping camera is ensured, the real-time performance of measurement and the comprehensiveness of subsystem monitoring are realized at the same time.

The invention discloses a diamond microstructure precision grinding system and method with visual positioning, and the method comprises the following steps: collecting a microscopic visual image, calibrating a visual coordinate system and a machine tool coordinate system, and building a unified positioning coordinate system; extracting composite visual features, constructing an energy field, performing adaptive partitioning, and generating risk partitioning codes; the risk partition codes are input into a double-flow network, grinding signals are input into grinding branches, and parameter tracks are generated; precisely grinding according to a segmented parameter track, reducing the cutting depth and the speed in a high potential energy area, and increasing the speed in a low potential energy area; after grinding, obtaining a three-dimensional shape, performing error analysis and energy spectrum generation, and mapping to risk partition coding; and according to the error energy and the energy field, the grinding priority is judged, and high-priority partitions are selectively ground and remeasured. According to the method, the visual potential energy field is constructed and the improved double-flow neural network is fused, so that accurate positioning, self-adaptive grinding control and high-precision micro-area grinding of the diamond microstructure are realized.

The present application discloses an image processing method, apparatus and system based on artificial intelligence, and a storage medium. The method comprises: acquiring a calibration image of a calibration apparatus, wherein the calibration apparatus comprises a pattern plate with a plurality of patterns; performing feature extraction on the calibration image to obtain a first feature map; inputting the first feature map into a frame regression network to output a plurality of regions of interest corresponding to the calibration apparatus in the first feature map; generating a preset number of candidate frames for each region of interest on the basis of a preset prior frame, the size of each candidate frame being the same as that of the preset prior frame, and the preset number being determined on the basis of the number of the patterns; and inputting all the candidate frames and the first feature map into a center regression network to obtain image center point coordinates of each pattern in the calibrated image. The solution can obtain clear imaging edge information, even if the imaging quality is poor, an accurate candidate region can be obtained, and the detection precision of image recognition is significantly improved.

The invention relates to the technical field of computer vision and intelligent monitoring, and particularly discloses a pavement ponding detection method based on image calibration and dynamic region segmentation, which comprises the following steps of: firstly, selecting a reference frame image in a ponding-free or slight ponding state from a road monitoring video, and manually marking a ponding region to generate a reference mask; the method is used as a unified detection reference standard. And then, frame extraction is performed on a to-be-detected video, pixel-level identification is performed on each frame of image by using a pre-trained semantic segmentation model, a water accumulation area of a current frame is automatically extracted, and a current water surface mask is generated. And dynamically judging the diffusion or fading trend of the accumulated water by calculating the difference value between the pixel area of the current frame accumulated water area and the pixel area of the reference mask accumulated water area. According to the method, the defects that a traditional sensor is high in deployment cost and complex in maintenance and an existing visual method is prone to being interfered and cannot be quantitatively changed are overcome, and low-cost, high-adaptability and quantifiable continuous automatic monitoring and early warning on the road surface ponding range is achieved.

The invention discloses a calibration method and system based on multi-scale feature fusion and an adaptive optimization algorithm, and belongs to the technical field of computer vision. The method comprises the following steps: collecting a plurality of images containing calibration modes; detecting and positioning feature points by adopting a multi-scale feature fusion technology, and distributing a confidence score for each feature point; carrying out feature point matching and removing abnormal values by using an improved RANSAC algorithm based on the prior geometrical relationship and confidence score; preliminarily calculating camera parameters; and a nonlinear optimization model fusing confidence weighting and adaptive regularization is constructed, joint optimization is performed on camera parameters, and a high-precision calibration result is output. According to the method, through an optimization mechanism of multi-scale feature fusion and confidence guidance, the problems of unstable feature point detection, abnormal value sensitivity and insufficient precision of a traditional method in a complex environment are effectively solved, and the accuracy, robustness and automation degree of image calibration are remarkably improved.

The invention discloses a semi-supervised non-contact neonatal jaundice home intelligent early warning method based on image calibration, and the method comprises the steps: building a camera response function database, and building a color constancy depth model and a skin region segmentation network; designing a strong and weak enhancement strategy by using a large amount of label-free home data, constructing a time and context comparison learning module, and performing self-supervised training on a feature encoder; a small amount of labeled hospital data and a large amount of unlabeled family data are combined, and semi-supervised fine tuning is realized through pseudo label generation and supervised comparative learning; image sequences continuously uploaded by a user are input into the model, multi-day prediction of the bilirubin level is achieved, prediction uncertainty is quantified in combination with a Bayesian method, the risk boundary is dynamically adjusted, and personalized early warning is provided. According to the method, a user does not need to use a physical colorimetric card, color measurement errors caused by model differences of mobile equipment and variability of household illumination are inhibited from a data source, and the accuracy and robustness of subsequent jaundice assessment are improved; through a training framework combining self-supervised pre-training and semi-supervised fine tuning, the understanding ability and generalization performance of the model for the sequential characteristics of jaundice are improved.

The invention discloses an image processing system and method suitable for a turbid water area, and belongs to the technical field of image processing. The system comprises an underwater camera component for collecting original image data of a turbid water area in real time and an embedded image processing unit. The embedded image processing unit comprises a preprocessing module used for carrying out noise suppression and image calibration processing on original image data, and a physical parameter estimation module used for estimating environment background light intensity of a current water area, a water body attenuation coefficient and a distance map of an object in a scene and calculating initial transmissivity. The scattering correction module is used for removing back scattering and preliminarily compensating a color channel based on a physical imaging model in combination with physical parameters, and the deep learning enhancement module is used for improving image local contrast, adjusting brightness and finely correcting colors by adopting a lightweight deep convolutional neural network. According to the image processing system and method suitable for the turbid water area, the image definition and color of the turbid water area can be improved, the real-time performance is high, and the system and method are suitable for different water area environments.

According to the image calibration and splicing method based on sub-pixel reconstruction and elliptic coefficient weighting, a super-resolution reconstruction module is introduced before image array splicing, the splicing precision is improved through sub-pixel-level image registration, and the splicing precision better than that of a single pixel unit can be achieved. Aiming at the problems of edge dislocation and stiff transition existing in multi-camera array system view field splicing, the invention provides a weighted splicing algorithm based on an elliptic coefficient, and the algorithm constructs an elliptic gradient weight model, realizes smooth transition of an image boundary region, and remarkably improves the overall visual effect of a panoramic image.

The invention discloses an image multi-target detection system and method based on a channel attention mechanism, and relates to the technical field of neural network image processing. The system comprises an image processing module, an image calibration module, a bounding box screening module and a multi-target detection module, wherein the image processing module performs automatic image enhancement on an original image based on image enhancement parameters obtained based on historical image data and contrast credibility parameters, and then performs feature extraction to obtain an original feature map of each channel; the image calibration module dynamically corrects the weight ratio of each channel based on the detection target parameter, and calibrates the original feature map of each channel according to the weight ratio; a bounding box screening module performs classification regression processing on the calibrated feature map to obtain each potential target, and performs confidence evaluation and similarity evaluation on each potential target based on a confidence score, a target feature vector and a target position vector to obtain each final multi-target; and the multi-target detection module performs multi-target detection based on each final multi-target.

The invention discloses a fusion calibration method for semiconductor measurement equipment, and the method comprises the steps: S1, executing the calibration of an optical navigator, so as to establish a first conversion relation between a CCD camera image coordinate system and a workpiece table coordinate system; s2, executing SEM image calibration to establish a second conversion relation between an SEM image coordinate system and a workpiece table coordinate system; s3, based on the first conversion matrix generated in S1 and the second conversion matrix generated in S2, realizing fusion calibration of a CCD camera image coordinate system, an SEM image coordinate system and a workpiece table coordinate system; wherein fusion calibration is used in semiconductor wafer measurement, initial positioning is carried out through a large view field of a CCD camera so as to distinguish repeated patterns, fine positioning is carried out through high resolution of an SEM image, and nm-level precision is achieved. The method has the advantages of remarkably improving the precision, stability and efficiency of semiconductor measurement equipment and the like.

The invention relates to an industrial part measuring method, device and system and computer equipment. The method comprises the following steps: acquiring calibration images of a calibration plate shot by cameras distributed at a plurality of different visual angles; the calibration plate comprises a plurality of patterns with fixed positions; according to an image area corresponding to a pattern contained in the calibration image, determining a first coordinate of the pattern contained in the calibration image in a calibration plate coordinate system corresponding to the calibration plate; acquiring a second coordinate of an image area corresponding to a pattern contained in the calibration image in an image coordinate system corresponding to the camera; determining a conversion relation between an image coordinate system corresponding to the camera and a calibration plate coordinate system corresponding to the calibration plate according to the first coordinate and the second coordinate; the target image shot by each camera to the industrial part to be measured is obtained, the feature points in each target image are projected to the calibration plate by using the conversion relation, and the target measurement assembly of the industrial part is measured according to the projection result, so that the measurement accuracy of the industrial part can be improved.

The application provides a calibration method and device of a vehicle-mounted surround view system, and relates to the technical field of intelligent vehicles. The method comprises the following steps: acquiring an image of a calibration object collected by a vehicle-mounted camera, the calibration object comprising multiple regions, at least one region comprising a pattern containing identification information, identifying the identification information of the pattern and the two-dimensional coordinates of the corner points of the pattern, and determining the calibration external parameters of the camera according to the identification information of the pattern and the two-dimensional coordinates of the corner points. Thus, the external parameter calibration of the camera is performed by identifying the corner points of the pattern of the calibration object, the distortion generated when the edge region of the image is identified is reduced, the requirement for ambient light and the arrangement difficulty of the calibration object are reduced, the identification rate of the corner points is improved, and the efficiency and accuracy of the calibration are improved.

The invention provides a calibration method, a measurement method, electronic equipment, a program product and semiconductor equipment, which can be used in the technical field of optical measurement. The method comprises the following steps: simulating to obtain a standard Mueller matrix based on standard parameters of a tested sample and optical parameters of optical measurement equipment; obtaining a far-field simulation image corresponding to the optical measurement equipment based on the standard Mueller matrix and the device parameters of the optical measurement equipment; performing calibration processing on the far-field simulation image based on each image calibration strategy in a preset calibration strategy library to obtain a corresponding calibration image; respectively resolving each calibration image to obtain a corresponding first Mueller matrix; and based on the difference between each first Mueller matrix and the standard Mueller matrix, screening out a corresponding optimal calibration strategy from a calibration strategy library. According to the method, the far-field simulation image acquired by the optical measurement equipment is calibrated, so that the calculation precision of the Mueller matrix is improved, and the measurement precision of the optical measurement equipment is improved.

This invention discloses a method and device for monitoring track alignment based on binocular vision measurement, comprising: fixing markers with coded images to each track segment; moving a binocular camera along the measurement track and taking pictures at different measurement points; preprocessing the captured image sequence and performing target detection to extract the coded images; calibrating the internal parameters of the binocular camera and the external parameters at each measurement point; obtaining the three-dimensional coordinates of each marker in the local binocular coordinate system based on the internal parameters of the binocular camera, and then converting them into the three-dimensional coordinates of each marker in the global coordinate system using the external parameters of the binocular camera; optimizing all the three-dimensional coordinates of the markers with unique labels in the global coordinate system using bundle adjustment to obtain the optimized three-dimensional coordinates of all markers in the global coordinate system, and then assembling them in order to obtain the three-dimensional alignment curve of the measurement track. This invention has high accuracy and is unaffected by environmental interference.

The invention discloses an orthopedic patient wound nursing method based on visual image processing, and relates to the technical field of wound nursing, and the method comprises the following steps: constructing a patient information database for orthopedic patients based on basic information of the patients, and recording the basic information of the patients and wound information, acquiring daily images of the orthopedic patient after wound suture through image acquisition equipment, and recording the daily images in a patient information database; for a wound image in a patient information database, feature pixels of the wound image are extracted and projected into a blank image, and initial wound line pixels and initial thread end line pixels are calibrated for an initial image; numbering the thread ends and recording initial thread end line pixels and the number of the pixels and initial wound line pixels and the number of the pixels; the wound healing state is monitored through visual image processing, the stitch removing opportunity is dynamically judged, the individual difference of patients is adapted, and the wound nursing efficiency of the orthopedic patients is improved.

The invention discloses a production device and method for a high-toughness wear-resistant pavement composite building material. The production device comprises a crushing box, a glass crushing mechanism, a separation box, a label separation mechanism and a separation and adsorption mechanism, and the separation and adsorption mechanism is arranged on the side wall of the separation box; a stirring box is arranged at the bottom of the separation box to receive the glass material after label separation; an auxiliary material feeding channel is arranged on the side wall of the stirring box, and a discharging pipeline is arranged at the bottom of the stirring box; and the production stirring mechanism is arranged in the stirring box. Images of the glass fragments are obtained in real time through an industrial camera installed at a feeding opening of the separation box; according to the method, processing steps including image acquisition and preprocessing, image calibration and noise elimination, and image segmentation and feature extraction are executed, and finally, driving instructions are sent to an adjusting motor, a first motor and a second motor, so that the distance between rubbing rollers is accurately adjusted and controlled, and the high toughness and high wear resistance of a subsequent composite building material are improved.

A time sequence brain image calibration method based on multi-task adversarial learning belongs to the technical field of medical images, and comprises the following steps: pre-processing a pre-acquired time sequence brain image to remove irrelevant information; a brain image phenotype calibration network is divided into two sub-tasks (local detail reconstruction and global structure transformation), learning is carried out in different modes, and a discriminator in an adversarial learning strategy is adopted to restrain and generate a better calibration brain image. The method is very valuable for improving the brain image and gene association performance, and the state of the human brain can be effectively observed and understood.

The invention discloses an external parameter calibration method based on camera and screen integration and a related device. The method comprises the following steps: determining a placement scheme of a plurality of calibration plates according to the type of a camera; arranging a plane mirror with a first size at the target distance of the screen; a calibration image collected by the camera according to the placement scheme and the plane mirror is obtained, the calibration image comprises a plurality of inclined calibration plate images located around the screen and a first horizontal calibration plate image located in the middle of the screen, and the first edge of the first horizontal calibration plate image is parallel to the screen edge corresponding to the first edge; the first edge is any edge of the first horizontal calibration plate image; determining internal parameters of the camera according to the calibration image; determining reference external parameters of the first horizontal calibration plate image according to the internal parameters; and adjusting the reference extrinsic parameter according to the target distance to obtain a target extrinsic parameter. According to the invention, efficient calibration of external parameters between the camera and the screen is realized.

The present application relates to the technical field of image calibration, and discloses a real-time image calibration system and method for a code jet printer based on edge computing, which comprises the following steps: firstly, a composite parameter matrix is constructed, no-load error analysis is completed, a compensation matrix and a dynamic weight distribution function are generated; secondly, a multi-scale gradient feature pyramid is constructed according to the composite parameter matrix, a feature correlation degree heat map is generated by fusing the features, and a model is established after correction to predict the ink diffusion trajectory; finally, a control signal is generated by combining the compensation matrix and the prediction result to control the motion pose of the nozzle; the method realizes accurate control of the code jet process by comprehensively considering multiple factors, significantly improves the precision and quality of the code jet pattern, effectively reduces the waste rate, and improves the production efficiency.

The application relates to the technical field of medical image processing, and discloses a needle insertion positioning device, a control method thereof and a storage medium. The needle insertion positioning device comprises an ablation needle, a magnetic calibration module and an auxiliary needle insertion module. The magnetic calibration module is used for acquiring a three-dimensional image model corresponding to a target object, searching for magnetic calibration position information and image calibration position information according to the three-dimensional image model, and constructing a conversion relationship from a magnetic positioning coordinate system to an image coordinate system of the three-dimensional image model according to the magnetic calibration position information and the image calibration position information. When the magnetic calibration module and the ablation needle are respectively detachably arranged in the auxiliary needle insertion module, the auxiliary needle insertion module acquires magnetic positioning information corresponding to the magnetic calibration module, converts the magnetic positioning information into needle insertion pose information of the ablation needle on the three-dimensional image model by using the conversion relationship, and displays the needle insertion pose information. The application realizes accurate positioning of needle insertion of the ablation needle and an action area of the ablation needle, so that the needle insertion direction and position of the ablation needle can be adjusted.

This application relates to a fault diagnosis method for abnormal edge trajectory of wind turbine blades. It employs an improved wavelet packet multi-scale analysis of blade operating state images for filtering and noise reduction; it obtains blade trajectory edge features through invariant moment features to achieve translational, scaling, and rotational invariance recognition; and it utilizes a particle swarm optimization algorithm to obtain images with large edge trajectory deviations, calibrating abnormal states for fault diagnosis. This method solves the problems in existing technologies, such as the inability of edge detection operator thresholds to adapt, poor noise resistance, and difficulty in extracting abnormal blade edge trajectory features, leading to ineffective fault diagnosis. It improves fault diagnosis accuracy and, by filtering redundant data through the optimization algorithm, balances the accuracy and efficiency of intelligent recognition, providing reliable technical support for wind turbine blade fault diagnosis.

This invention provides a SA-ISAR autofocusing and calibration method based on sparse LVD, comprising: constructing an echo model of a sparse aperture maneuvering target; obtaining a sparse signal recovery model of the linear frequency modulated signal in the CFCR domain during the energy accumulation process by performing energy accumulation based on the CFCR domain under the echo model; solving the sparse signal recovery model based on the FSBL-LVD-CTF method to obtain an ISAR image; and performing lateral calibration processing on the ISAR image using the FSBL-LVD-CTF method to obtain the ISAR image calibration result. In this invention, the FSBL-LVD-CTF method combines the strong robustness of sparse Bayesian learning and the efficient processing capability of Lf distribution transformation for linear frequency modulated signals, while also suppressing cross terms through cross-term filtering of CTF to ensure reliable sparse recovery results, thereby improving the accuracy and robustness of ISAR imaging.

The invention discloses a structured light camera calibration method and device, electronic equipment, a storage medium and a product. Relates to the technical field of image processing. The method comprises the steps that a calibration plate is moved to different positions, calibration plate images of all the positions are obtained, all identification points in the calibration plate correspond to a circle of a first radius and a circle or concentric circle of a second radius, and the first radius is larger than the second radius; determining circle center coordinates of the feature points in the calibration plate according to the calibration plate image at each position; performing stereoscopic vision calibration according to the circle center coordinates to obtain camera parameters; and if the error between the projection point obtained based on the camera parameters and the circle center coordinate meets the requirement, the structured light camera calibration is completed. According to the scheme, a checkerboard calibration board is upgraded into a composite calibration board composed of circles and concentric circles of different sizes, stereoscopic vision calibration is carried out through the feature points, the calibration result is verified based on the projection points, light spot interference can be reduced, errors can be reduced, and the robustness and calibration precision of the calibration process are improved.

The invention discloses an HUD image display calibration method and device and a storage medium, and relates to the technical field of HUD image calibration, and the method comprises the following steps: obtaining the eye movement ranges of different users in different use regions when driving a vehicle according to the use regions of the vehicle, and recording the eye movement ranges as initial eye movement range data; performing eye movement range preprocessing on the initial eye movement range data of the different use areas to obtain reference eye movement range data of the different use areas; performing eye box range acquisition processing to obtain eye box range position data of different use areas; hUD image display is calibrated in the vehicle manufacturing process based on the corrected eye box data of the different use areas; the method is used for solving the problem that when a vehicle-mounted HUD is calibrated in the vehicle manufacturing process through an existing HUD image calibration technology, a calibration eye box cannot be adjusted according to the eye movement range of a user in a vehicle using area, and therefore the calibrated HUD can adapt to users in different areas.

The invention relates to an optical axis calibration method and system, a ghost image measurement method and computer equipment. The method is applied to an optical axis calibration system comprising an image acquisition device which is subjected to pose adjustment by a first adjustment mechanism and an adjustment jig which is subjected to pose adjustment by a second adjustment mechanism and is provided with a display module to be tested, and comprises the following steps: controlling the first adjustment mechanism and / or the second adjustment mechanism to perform rotation adjustment, aligning a cross calibration image displayed by the display module to be tested with an image reference cross of the image acquisition equipment; a second adjusting mechanism is controlled to perform translation adjustment, so that multi-order ghost images formed by the ghost image calibration image displayed by the display module to be tested in the picture of the image acquisition equipment are regularly distributed; and the first adjusting mechanism is controlled again to perform rotation adjustment, so that the cross calibration image displayed by the display module to be tested is aligned with the image reference cross of the image acquisition equipment. By adopting the method, ghost image interference can be reduced, the optical calibration accuracy is optimized, and the ghost image measurement accuracy is improved.

The application discloses an unmanned aerial vehicle inspection system and method for intelligent street lamps based on multi-source video fusion, relates to the technical field of unmanned aerial vehicle inspection, and comprises an inspection data acquisition module, an inspection image analysis module, an inspection mode switching module and an inspection task dynamic evaluation module; the inspection data acquisition module, the inspection image analysis module, the inspection mode switching module and the inspection task dynamic evaluation module are connected in communication with each other; wherein the inspection data acquisition module is used for flying inspection of street lamps in a target inspection area one by one by means of an unmanned aerial vehicle provided with a visible light camera, and setting image calibration array points to match the accuracy of inspection image acquisition; the inspection image analysis module is used for processing and analyzing collected image data; and the application solves the problem that the existing multi-source image fusion processing mode greatly increases the inspection time and data processing burden.

The disclosure provides a video fusion method, and particularly relates to the technical fields of automatic driving and image processing. The specific implementation scheme is as follows: obtaining image calibration coordinates of a plurality of fisheye cameras, fusion image coordinates, and a plurality of videos captured by the plurality of fisheye cameras, wherein the fusion image coordinates are obtained by fusing all the image calibration coordinates; encoding the image calibration coordinates to obtain coordinate information encoding; obtaining weight parameters of image pixels of each image in the plurality of videos based on the fusion image coordinates; selecting image pixels of each image in the plurality of videos based on the coordinate information encoding; and calculating a fusion image corresponding to all the images in the plurality of videos based on the weight parameters of each image and the image pixels of each image. The embodiment improves the real-time performance of video fusion.

Various examples of systems and methods are provided for imaging calibration for slide processing. In one example, among others, a system for processing microscope slides includes a light source; an imaging device comprising a lens; and a slide positioner that can position the ground-glass portion of a slide between the light source and lens. Processing circuitry of the system can acquire an image of at least a section of the ground-glass portion at an initial position; analyze contrast of adjacent pixels with respect to a defined contrast characteristic; iteratively advance the lens and acquire additional images based upon analysis of contrast of adjacent pixels with respect to the defined contrast characteristic; and identify an optimal focal location of the lens based upon the defined contrast characteristic. Subsequent image acquisition via the lens can be based at least in part upon the optimal focal location.

Various examples of systems and methods are provided for imaging calibration for slide processing. In one example, among others, a system for processing microscope slides includes a light source; an imaging device comprising a lens; and a slide positioner that can position the ground-glass portion of a slide between the light source and lens. Processing circuitry of the system can acquire an image of at least a section of the ground-glass portion at an initial position; analyze contrast of adjacent pixels with respect to a defined contrast characteristic; iteratively advance the lens and acquire additional images based upon analysis of contrast of adjacent pixels with respect to the defined contrast characteristic; and identify an optimal focal location of the lens based upon the defined contrast characteristic. Subsequent image acquisition via the lens can be based at least in part upon the optimal focal location.

The invention provides an image calibration method and device for a track inspection system, and the method comprises the steps: obtaining an initial sawtooth target image, and calculating a definition evaluation value through a spatial convolution method; an optimal focusing position is determined through curve fitting, and an optimal focusing sawtooth target image is obtained; extracting central pixel coordinates of laser stripes to obtain light stripes, and determining angular point information through an angular point extraction algorithm; generating a grid point array according to the angular point information, and calculating camera calibration parameters in combination with the angular point information and the grid point array; back-projecting the center pixel coordinate of the light bar to a world coordinate system through the calibration parameter to obtain a calculated physical coordinate; comparing and calculating the physical coordinates with preset coordinates of the target, and determining final calibration parameters; according to the method, the anti-interference characteristic of the triangular edge of the sawtooth target is utilized, the method can be adapted to the complex field environment of the rail detection system, accurate solving of calibration parameters is achieved, manual dependence is reduced, and technical support is provided for high-precision detection of the rail detection system.