346 results about "Monocular camera" patented technology

The invention discloses a rapid robust monocular vision inertial positioning method and system. The method comprises the following steps: configuring IMU and monocular camera sensor parameters; preprocessing the IMU data; iMU attitude initialization is carried out; image features of the monocular camera are extracted, abnormal matching points are removed, and IMU pre-integration is carried out; monocular vision initialization is carried out; odometer attitude optimization: judging whether the feature points are mismatched or not based on an IMU pre-integration result, constructing a feature point re-projection error jacobian matrix, and performing blocking and diagonalization processing on the re-projection error jacobian matrix to respectively optimize inverse depths and image frame attitudes of the feature points; selecting a key frame based on a key frame identification rule after the current frame attitude update is completed; and judging according to the current frame, and removing a certain frame in the sliding window to reserve a sliding window space for adding the latest frame. According to the method, the inverse depth of the feature points and the image frame attitude are optimized respectively based on the counterweight projection error Jacobian matrix partitioning and diagonalization processing, and the calculation complexity is reduced.

Bird's eye view (BEV) semantic mapping systems and methods are provided. A method includes receiving an image captured by a monocular camera having a first point of view (POV) of an environment including a plurality of features. The method further includes processing, by an artificial neural network (ANN), the captured image to generate a semantic map for the captured image, the semantic map associated with a second POV different from the first POV. The features exhibit a uniform scale in the semantic map. Additional methods and associated systems are also provided.

The invention discloses a method and a system for quickly positioning the vertex of a suspension arm based on laser radar point cloud calibration monocular depth estimation, and the method comprises the steps: firstly constructing a power transmission line external damage prevention monitoring and shooting scene in a simulation environment, collecting data, and training a lightweight monocular depth estimation network suitable for the power transmission line scene and a monitoring and shooting visual angle; secondly, constructing a truncated cone containing construction machinery by using rapid projection between the monocular camera and the laser radar point cloud, performing depth scale calibration on a monocular depth estimation result by taking the laser radar point cloud as a reference, and performing rapid densification on sparse point cloud in the truncated cone; and finally, accurately positioning the three-dimensional coordinates of the vertex of the suspension arm in the dense virtual point cloud in the crane truncated cone. The method is suitable for a vision and laser radar integrated power transmission line external damage prevention edge monitoring and shooting device, supports one kind of related applications of accurate distance measurement between the suspension arm vertex and the power transmission line, can overcome the problem of sparse point cloud of a long-distance suspension arm, and improves the three-dimensional positioning precision of the suspension arm vertex.

The invention relates to the technical field of optical three-dimensional measurement and metering, and discloses a single-camera three-dimensional coordinate measurement method and system based on full-field distance measurement, and the method comprises the following steps: S1, building a calibration field containing a control point, shooting an image of the calibration field through a monocular camera, extracting the image coordinate of the control point, carrying out the camera calibration, and obtaining a calibration field; obtaining internal parameters and external orientation parameters of the camera; and S2, controlling the laser range finder to aim at the control point in the calibration field through the non-orthogonal double-axis turntable. Submillimeter-level high-precision measurement can still be realized under the condition of a short baseline, and the problems that the precision in the depth direction is insufficient, the baseline requirement is long, parameters are easy to drift and the like in traditional stereoscopic vision measurement are effectively solved. By introducing a non-orthogonal double-shaft turntable and a visual guidance laser aiming mechanism, autonomous, rapid and accurate measurement of multiple measurement points in a large field of view is realized, and the automation degree and reliability of measurement in a complex environment are remarkably improved.

The invention belongs to the technical field of computer vision, and particularly discloses a monocular 3D object detection method for realizing depth enhancement based on a visual basic model, electronic equipment and a readable storage medium, and the method comprises the steps: S1, building a data set: employing a monocular camera to collect a pavement scene, and obtaining an RGB image in the pavement scene; s2, image preprocessing: preprocessing the RGB image for subsequent feature extraction and depth estimation; s3, performing feature extraction by adopting a dual-backbone network: performing visual semantic feature extraction on the preprocessed RGB image by using DINOv2; performing depth feature extraction on the preprocessed RGB image by using a DPT head; s4, generation of depth perception query points: inputting the visual semantic features and the depth features into a DETR network to generate the depth perception query points; and S5, target detection output: using an MLP-based detection head to obtain information of the category, the size, the center point position, the depth, the 3D size and the direction of the object.

The invention provides a 3D anti-collision detection method and system based on monocular vision, and the method comprises the steps: obtaining the image data of a front environment collected by a vehicle-mounted monocular camera in real time, and carrying out the preprocessing of the image data, and obtaining the standardized image data; generating an optimized absolute depth map by fusing a first depth estimation result and a second depth estimation result based on the standardized image data; detecting a 3D obstacle based on the optimized absolute depth map and scene semantic information to obtain related information of at least one target obstacle; and performing collision risk assessment according to the related information, and triggering early warning when the risk meets an early warning condition. The invention provides a high-precision and high-robustness monocular 3D anti-collision detection scheme, monocular depth estimation based on deep learning, semantic segmentation and a dynamic safety model are combined, and an end-to-end anti-collision system with physical significance is formed.

The invention discloses a low-altitude unmanned aerial vehicle autonomous cruise method and system based on cloud edge basic model collaboration, and the method comprises the following steps: S1, collecting an environment RGB image through an airborne monocular camera of an unmanned aerial vehicle, and carrying out the preprocessing of the image, and obtaining a preprocessed gray image; s2, a neural scheduler based on deep reinforcement learning generates a scheduling instruction according to the environment data and the network state reasoned by the navigation model at the previous moment; s3, generating a preliminary flight instruction; s4, generating an optimized flight instruction; s5, generating a structured flight instruction; and S6, the unmanned aerial vehicle executes the preliminary flight instruction or the optimized flight instruction or the structured flight instruction. According to the invention, through dynamic on-demand cooperation and intelligent scheduling of the end-edge-cloud three-level model, resource consumption and delay are substantially reduced, and high-robustness and high-safety autonomous cruise of the unmanned aerial vehicle in a complex open environment is realized.

Provided are systems and techniques for automated navigation of vehicles, such as drones. The systems generally include processing unit(s) that, collectively, perform several steps. Such steps include generating metric depth estimates, using a pre-trained model, for each pixel in received image(s) from a monocular camera, or transformed image(s) based on the received image(s). Such steps may also include generating a pose estimate from visual odometry, then generating a truncated signed distance function representation of an environment based on the absolute depth estimates and the pose estimate. The steps may include creating and / or updating a local map based on the truncated signed distance function representation. The steps may include plan a collision-free route towards a goal based on the local map. This may include using motion primitives, which may be generated in a single offline step and stored in a trajectory library.

The invention relates to the technical field of computer vision and image processing, in particular to an end-to-end image registration method based on a monocular camera and micro rendering. The objective of the invention is to solve the problems of low registration precision and insufficient real-time performance caused by complex calculation, serious error propagation and poor adaptability to multi-modal data in a traditional image registration method. According to the main scheme, the method comprises the steps of extracting structured boundary information of a target object in an input image through a contour segmentation model, and generating a binary mask image; based on the contour segmentation result, estimating a 6D pose parameter of the target object through a 6D pose estimation deep network fusing a convolutional neural network CNN and a Transform module; and inputting the 6D pose parameters into a micro-renderable module, generating a prediction image, comparing the prediction image with an original input image, and performing end-to-end optimization and adjustment on model parameters to realize high-precision registration.

The invention provides a large-view-field multi-view camera calibration method, system and device and a medium. The method comprises the following steps: acquiring a multi-view-field target plate image in a large-view-field plane by using a monocular camera; the target plate comprises a plurality of coding targets; obtaining target feature points of the plurality of coding targets based on the target plate image so as to correspondingly obtain target code values based on the target feature points; and obtaining a three-dimensional point coordinate of each target feature point based on each target code value, and carrying out multi-view camera calibration based on the three-dimensional point coordinates. According to the invention, the large-view-field multi-view camera can be calibrated, and the calibration precision is high.

The specification discloses an image recognition-based robot charging method, system and electronic equipment, which has the advantages of low cost, high precision, wide recognition range, etc. The method comprises: acquiring a work scene image by using a monocular camera on the robot, and extracting a charging pile image from the work scene image; performing image recognition on the charging pile image to obtain position information of a feature icon on the charging pile in the charging pile image; judging a positional relationship between the robot and the charging pile according to the position information; performing path planning according to the positional relationship, generating charging path parameters, and controlling the robot to move to the charging pile for charging based on the charging path parameters. The system comprises: a charging pile image extraction module, a feature icon positioning module, a robot positioning module, and a path planning control module. The processor in the electronic equipment implements the method when executing a program.

The invention discloses a multi-modal three-dimensional point cloud semantic segmentation method based on adaptive noise filtering, and belongs to the technical field of automatic driving environment perception. The method comprises the following steps: firstly, acquiring data by using a laser radar and a monocular camera, and constructing a multi-modal panoramic feature tensor containing a geometric structure and color textures through projection and mapping; extracting shallow geometric distribution features through a residual context module, and extracting multi-scale environment features through an expanded residual encoder; performing global context aggregation by using a self-attention mechanism of a Transform architecture, and establishing a full-image pixel dependency relationship to make up for a convolution locality defect; in the decoding stage, a channel cross fusion attention module (CCA) is adopted to process deep semantic features and shallow jump connection features, and a channel weight mask is dynamically generated to adaptively screen effective features and suppress high-frequency noise; and finally, outputting a two-dimensional semantic segmentation result and back-projecting the result to a three-dimensional space to obtain a semantic point cloud. According to the method, through global semantic integration and local detail screening, the problems of terrain misjudgment and noise interference of severe weather (such as rain, snow and dust) in a cross-country scene are effectively solved, and the robustness and precision of automatic driving perception are remarkably improved.

The invention discloses a displacement monitoring method and system based on a movable monocular camera, and particularly relates to the technical field of camera displacement monitoring. The method comprises the following steps: initially and synchronously acquiring images of a reference target and a to-be-measured target, and acquiring an initial pixel size and an initial center coordinate of the reference target and an initial center coordinate of the to-be-measured target; when the pose of the monocular camera changes, the image is collected again, and the pixel size and the center coordinate after the reference target changes are obtained; calculating an image scaling factor, determining an image coordinate transformation parameter, and constructing a dynamic coordinate transformation relation to obtain theoretical pixel coordinates of the target to be measured; and calculating the displacement according to the deviation between the actual pixel coordinate and the theoretical pixel coordinate of the target to be measured. According to the invention, stable calculation of the displacement of the to-be-measured target can be realized under the condition that the pose of the monocular camera changes, and the applicability and measurement consistency of the displacement monitoring process are improved.

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

The invention provides a vision-based auxiliary-mark-free port machinery equipment movement distance detection method and system, and relates to the field of computers.The method comprises the steps that monocular cameras are installed on lifting appliances on the two sides of a gantry crane, the gantry crane is controlled to move along a preset track, and a calibration image sequence containing ground linear features is obtained; a radial distortion parameter of the camera is calculated, a nonlinear mapping model from a pixel coordinate system to a world coordinate system is established, a ground unshielded area is selected as a dynamic monitoring area, and a texture richness thermodynamic diagram is generated; after receiving a measurement instruction of a scheduling system, initializing an optical flow accumulator, loading a current feature point set, synchronously collecting monocular video streams, and distributing the monocular video streams to at least three preprocessing threads to execute differential image enhancement; optical flow calculation is executed on all preprocessing results in parallel, a multi-mode optical flow vector field is generated, and three-level optical flow screening is implemented. The precision and the sensitivity of small-range movement measurement of the gantry crane are improved through an optical flow method, and the influence of environmental factors on a measurement result is reduced.

The invention discloses a digital human live broadcast microphone connection and video generation method based on computer vision, and belongs to the technical field of artificial intelligence, and the method comprises the following steps: 1, real-time vision capture and action expression driving: employing a computer vision tool to carry out face key point detection, facial feature points of lips, eyes and the like of a live anchor or a microphone-connected guest are accurately captured; and then a voice conversion model is matched, microphone-connected audio features and lip key point movement are bound, precise synchronization of the sound and the lip is achieved, and for limb actions, limb joint data can be collected through a monocular camera in combination with a posture estimation algorithm or a simple action capture device and mapped to a digital human skeleton model. According to the digital human live broadcast microphone connection and video generation method based on computer vision, through multi-dimensional technical innovation and systematic design, the problems of insufficient sense of reality, interaction lagging, poor scene adaptability and the like existing in current digital human application are effectively solved.

Methods, systems, mobile devices, and non-transitory computer-readable mediums for determining a scale of an object with a mobile device. The mobile device includes at least one object-facing camera (e.g., a monocular camera) and an away-facing stereo camera system. Information captured using the away-facing stereo camera system is used to estimate relative pose information for determining the scale of the object in images captured by the object-facing camera. The proposed system leverages the scene images surrounding the mobile device to resolve scale ambiguity and calculate the absolute scale.

The application relates to the field of image processing, in particular to an LED double-lamp recognition method and device based on a monocular camera, equipment and a medium. The method comprises the following steps: ensuring data real-time by collecting video frames at a preset frequency, accurately distinguishing constant light and flickering light spots by using bright spot recognition and feature screening, and effectively excluding environmental interference; accurately associating the double-lamp spots of the same vehicle in space and time by using an association matching technology, and improving the accuracy of target vehicle recognition; predicting the vehicle trajectory based on Kalman filtering and other algorithms, and comprehensively judging the line-crossing event by combining the line-crossing time difference and the speed condition, which not only improves the smoothness and anti-interference ability of vehicle trajectory prediction, but also enhances the reliability and accuracy of line-crossing detection, so that the method has high robustness and practicality in complex scenes.

The invention discloses a spacecraft ground full-physical simulation system attitude measurement calibration method and device, and belongs to the technical field of spacecraft measurement. The objective of the invention is to solve the problem of real-time measurement of the attitude of a spacecraft ground full-physical simulation system. A cross laser generator, a concentric circular ring coding target, a monocular camera and a processing unit are fixedly installed on a three-axis air bearing table, the monocular camera is fixedly arranged on one side of the cross laser generator, and the monocular camera is connected with the processing unit. Laser emitted by the cross laser generator vertically irradiates the plane of the concentric circular ring coding target, the concentric circular ring coding target adopts a concentric circular ring coding array design, and each group of concentric circular ring coding units is composed of a plurality of hollow circular rings which are concentrically arranged. And generating a code ID of each group of concentric circular ring coding units through radius proportion combination of the adjacent hollow circular rings. The method is stable in long-term operation precision, greatly reduces the workload of element installation and calibration, does not need a complex field calibration process, and is flexible in installation and deployment.

The application discloses a 4D video generation method fusing monocular vision and terrain elevation data, comprising the following steps: acquiring video frame images and corresponding camera pose data collected by a monocular camera; acquiring terrain elevation data of a target area, and constructing a three-dimensional elevation network model; based on the camera pose data, establishing a ray projection model for pixels in the video frame images, calculating the intersection of the rays and the three-dimensional grid model, and obtaining the reference distance from the monocular camera to the ground surface corresponding to the pixels; generating an initial relative depth map of the video frame images by using a depth estimation model; selecting high-confidence ground pixels from the video frame images as reference points, taking the reference distance corresponding to the reference points as the true value, and performing absolute scale correction on the initial relative depth map to obtain an absolute depth map; performing back projection calculation on the absolute depth map, the camera pose data and pixel color information to generate a three-dimensional point cloud, mapping the three-dimensional point cloud to a real geographic coordinate system, and generating 4D video data in time sequence.

The invention discloses a cavern excavation quality evaluation method based on monocular depth estimation, and the method comprises the steps: obtaining a three-dimensional laser point cloud of an excavated section of a cavern as a first point cloud model, carrying out the downscaling of the point cloud through combining with the image and pose information continuously collected by a monocular camera, and projecting the point cloud to an image to generate a sparse depth label; constructing a training set and training a convolutional neural network to generate a monocular depth estimation model; discretizing the design section to generate a design point cloud model; predicting a depth map by using the trained model, and reconstructing a second point cloud model in combination with the camera pose; the normal distance deviation between the actual point cloud and the design model is calculated through space matching, an excavation quality thermodynamic diagram is generated, and efficient and high-precision non-contact evaluation is achieved. According to the method, laser and visual advantages are fused, and the problems of low efficiency and high cost of a traditional method are solved.

The present application relates to the technical field of automation control, and especially relates to a box workpiece pose measurement method based on point features, comprising calibrating a monocular camera by Zhang Zhengyou calibration method, obtaining internal parameters and distortion parameters of the monocular camera; selecting four corner points of four box workpieces in a three-dimensional space as feature points in an image, establishing a world coordinate system, a camera coordinate system, an image coordinate system and a pixel coordinate system, and obtaining three-dimensional coordinates of the feature points in the world coordinate system; using the point features to perform image processing on the preprocessed image, obtaining two-dimensional coordinates of the feature points in the pixel coordinate system; combining the three-dimensional coordinates of the feature points in the world coordinate system, the two-dimensional coordinates in the pixel coordinate system and the internal parameters of the monocular camera, and using a PNP measurement method to solve the pose information of the box workpiece. The present application provides a solution to the problem that the existing pose measurement method is complicated and cannot measure the pose of small box workpieces.

The method for large scene real-time three-dimensional reconstruction based on 3R belongs to the field of computer vision and three-dimensional reconstruction technology, and comprises the following steps: acquiring images of an RGB monocular camera; constructing a multi-level reconstruction architecture of "ordinary frame-key frame-scene frame", and dynamically screening frame categories based on inter-frame point cloud overlap and reconstruction confidence; performing multi-frame three-dimensional reconstruction based on 3R on ordinary frames and related scene frames to generate point clouds in the same coordinate system; after a new key frame is confirmed, taking the scene frame as a medium, calculating a preliminary transformation between point clouds based on singular value decomposition, and then performing fine registration by using a GICP algorithm; and performing point cloud fusion by using a TSDF technology, and selectively locking high-quality areas to improve efficiency. The method breaks through the bottleneck of existing methods in terms of reconstruction accuracy and the like by innovatively integrating 3R multi-frame reconstruction, GICP registration and TSDF technology, and realizes large-scale scene three-dimensional reconstruction with high robustness, high accuracy and real-time performance.

The application discloses a driving environment perception method based on a vehicle-mounted monocular camera, which comprises structure reparameterization on an up-sampling module and automatic driving multi-task perception. Compared with common linear interpolation and transpose convolution, the application uses RepUpsample to improve the accuracy of the network model to a certain extent. In the semantic segmentation model task, compared with the accuracy performance of DeepLabv3, FPN and U-Net three models when using different up-sampling modules, RepUpsample as the up-sampling method can improve the performance of the semantic segmentation network in different network models, different up-sampling positions and different network scales. Compared with the bilinear interpolation algorithm, the average mIOU can be improved by 1.77%, and the average P.A. can be improved by 0.74%. Compared with the transpose convolution, the average mIOU can be improved by 1.16%, and the average P.A. can be improved by 0.35%.

The invention discloses an AUV (Autonomous Underwater Vehicle) pose correction method and system based on perspective conversion and a storage medium, and belongs to the field of underwater robots. The method comprises the steps that an underwater circular light source lamp array image is shot through a monocular camera, and initial circle center feature points are extracted after preprocessing and contour screening; using a P3P algorithm to preliminarily solve the relative pose and constructing a perspective transformation matrix; carrying out ellipse fitting on the contour and sampling on an ellipse, and projecting sampling points to a virtual plane through inverse perspective transformation; calculating an average circle center coordinate on the virtual plane, and re-projecting the average circle center coordinate to the image plane to obtain a corrected feature point; and finally, calculating a high-precision final pose by using the correction point through a P3P algorithm. According to the method, the virtual world plane is constructed by adopting a perspective conversion-based method with relatively small operand, the circle center coordinate is obtained in a circle correction mode by utilizing the characteristic that the inclination angle between the virtual world plane and the real world plane is relatively small, the influence of an eccentric distance phenomenon on the extraction of the circular target feature point is reduced, and the AUV pose resolving precision is effectively improved.

The application provides a kind of underdetermined modal identification method and device based on monocular camera, belong to modal parameter identification technical field, method includes: using monocular camera to collect multiple images including all feature targets, according to image acquisition vibration response signal, when vibration response signal is time-invariant vibration response signal, reconstruct time-invariant vibration response signal into compressed sensing model, the ESTD dictionary obtained by training time-invariant vibration response signal, according to ESTD dictionary and compressed sensing model, modal identification of time-invariant vibration response signal is realized, when vibration response signal is time-invariant vibration response signal, time-varying vibration response signal is divided into a finite time-invariant vibration response signal using sliding window, combined with the idea of transfer learning to obtain strong sparse ESTD dictionary, and then the modal identification of time-varying vibration response signal is obtained.The application can solve the underdetermined modal identification under time-invariant structure and time-varying structure, and improve the identification speed and precision of underdetermined modal identification parameters.

The invention discloses an edge computing device deployment method for realizing table tennis recognition based on RT-DETR, belongs to the technical field of crossing of computer vision and sports, and aims at deploying a complex deep learning model on a resource-limited edge computing device to meet the requirement of efficient and real-time object recognition. According to the method, the RT-DETR model is deployed by using an NVIDIA Jetson Orin Nano edge computing platform so as to carry out table tennis ball detection. According to the method, the unique Transform architecture advantages and the end-to-end detection capability of RT-DETR are utilized, delay fluctuation caused by non-maximum suppression (NMS) in a traditional anchor frame-based method (such as a YOLO series) is effectively avoided, and the GPU acceleration and TensorRT optimization capability of Jetson Orin Nano is fully utilized while extremely high accuracy is ensured, so that the reasoning speed is remarkably increased. The system is suitable for precise tracking and trajectory analysis of tiny objects in a rapid dynamic environment, and high-frame-rate detection and intelligent training assistance of table tennis balls are completed through a monocular camera.

The utility model discloses a replaceable bidirectional waterproof monocular camera, and relates to the technical field of industrial endoscopes. According to the device, the screwing pipe and the wear-resistant pipe are combined or disassembled by screwing the screwing pipe, so that the components in the internal space of the wear-resistant pipe can be operated to complete replacement or maintenance of the internal components, and the wear-resistant pipe and the screwing pipe are screwed by screwing the wear-resistant pipe and the screwing pipe. A first waterproof O-shaped ring is fixed in the mode that the outer wall of a screwing pipe and the outer side wall of a wear-resisting pipe abut against and compact the first waterproof O-shaped ring, so that the first waterproof O-shaped ring is not prone to shaking or position deviation, meanwhile, the waterproof performance of the first waterproof O-shaped ring is improved, the waterproof effect of the device is better, and the service life of the device is prolonged. And a second waterproof O-shaped ring is arranged in a matched mode, the purpose of multiple-position waterproof is achieved, and therefore the effects of multiple-position waterproof and convenient replacement are achieved, and practicability is higher.

The invention provides a method and a system for quickly positioning railway intrusion personnel based on visual estimation, belongs to the technical field of target identification, applies an advanced monocular depth estimation and positioning method to rail transit, and solves the potential safety hazard of train operation caused by insufficient personnel intrusion position along the current railway. According to a pre-constructed target prior mapping relation between image pixel coordinates and actual geographic coordinates, after an invader is detected, the pixel coordinates of the invader are directly mapped to corresponding actual longitude and latitude positions; and if the target prior mapping information does not exist, calling a coordinate conversion algorithm according to the pixel coordinates to realize conversion from the pixel coordinates to latitude and longitude coordinates, realizing dynamic conversion from an image plane to a global geographic coordinate system, and obtaining actual latitude and longitude positioning of personnel invasion. According to the invention, high-precision positioning can be carried out on the invading personnel in the rail transit scene only by means of the monocular camera, so that the problem that personnel invasion is difficult to check in an existing system is solved, and the safe operation level of a railway is improved.

The invention belongs to the technical field of computer vision and automatic control, and particularly relates to a two-degree-of-freedom optical tracking method and system based on monocular vision, a calibration plate is arranged on a scanner, and a single calibration image containing the calibration plate is shot through a monocular camera; calibration point coordinates of the calibration image are extracted, the calibration image is input based on an ideal camera imaging model, the physical spacing of each calibration point, the image resolution, the pixel size and the lens focal length are known parameters, and the principal point coordinates, the pixel size ratio, the principal distance and the z-direction translation amount of the camera are estimated; introducing a comprehensive lens distortion model containing radial distortion, eccentric distortion and thin prism distortion, and obtaining an accurate radial distortion coefficient, an eccentric distortion coefficient, a thin prism distortion coefficient and rotation and translation amounts of the camera through decoupling calculation; theoretical pixel coordinates of the calibration points are obtained through reverse re-projection, and calibration precision is evaluated and optimized; the camera visual angle is adjusted through the holder control system, so that the scanner is located in the center area of the camera visual field.