96 results about "Hough transforms" patented technology

The invention provides an electric power line defect detection method and system based on visual identification, and relates to the technical field of line detection.The method comprises the steps that firstly, a visible light and infrared image dual-light registration and differential operation technology is adopted, and a fusion feature map capable of reflecting component thermal anomaly and material difference at the same time is generated; secondly, a black box type target detection model is abandoned in a part positioning link, but line segment screening and reconstruction are carried out by combining probability Hough transform with specific prior geometric knowledge of a power line, so that dependence on a large amount of labeled data is reduced, interpretability of a positioning process is enhanced, and the positioning accuracy is improved; according to the method, accurate areas of key components such as wire insulators can be extracted in a complex background, the concept of a probability saliency map is introduced in a defect identification core link, so that a defect area is enhanced and highlighted, and then a complete defect contour is determined by adopting an adaptive threshold segmentation and area growing algorithm; accurate mapping from pixel-level features to object-level defects is realized.

The invention provides an intelligent inspection system based on a robot and a camera, and relates to the field of intelligent manufacturing, the system adopts a four-layer architecture including a sensing layer, a transmission layer, a platform layer and an application layer, the sensing layer is composed of a fixed camera and an intelligent inspection robot; the transmission layer adopts optical fibers and 5G hybrid networking; the platform layer is integrated with a distributed server cluster, a data platform and an AI inference engine; the application layer provides a Web and mobile terminal management interface. The core of the system adopts a plurality of AI algorithms, including an instrument reading recognition algorithm based on Hough transform and CRNN, a leakage detection algorithm combined with background difference and an optical flow method, and a multi-device collaborative scheduling algorithm based on reinforcement learning, and constructs a digital twinborn inspection scene of virtual-real mapping. According to the invention, automatic and intelligent inspection and visual monitoring of power plant instruments, pipelines, power distribution rooms and other areas are realized, the inspection efficiency and accuracy are effectively improved, and the labor cost and the safety risk are reduced.

The invention discloses an automatic instrument reading method and system based on embedded machine vision. The method comprises the following steps: deploying an embedded image acquisition unit with wide dynamic imaging capability; performing multi-mode preprocessing of white balance correction, contrast enhancement and noise suppression on the image; self-adaptively identifying the instrument type and positioning a key area by using a lightweight convolutional neural network; hough transform and sub-pixel edge detection are adopted for a pointer type instrument to achieve high-precision angle analysis, and optical character recognition is completed for a digital instrument through a bidirectional long-short-term memory network; and finally, fusing multi-frame results through Kalman filtering, evaluating credibility, and outputting stable readings with timestamps. According to the technical scheme, high-robustness, high-precision and low-delay automatic reading can be realized at the embedded end, and the operation stability and the long-term adaptability of the system in a weak network environment are remarkably improved.

The invention provides a self-adaptive vacuum defoaming process optimization method based on an intelligent control system, and aims to solve the problems of micro-bubble residue, too high energy consumption and the like caused by the fact that a traditional defoaming technology cannot adapt to liquids with different viscosities, and accurate defoaming is realized through a multi-dimensional sensing technology and strategy generation and regulation. Presetting a high-precision vacuum parameter and a viscosity grading strategy; establishing a liquid characteristic vector model by adopting a YOLOv7 and Hough transformation algorithm, and matching a historical optimal strategy in combination with weighted cosine similarity; generating a defoaming strategy based on the pre-training network and a multi-objective optimization algorithm, and verifying the defoaming strategy in the virtual environment; defoaming is carried out in four stages, and efficient foam breaking is achieved through sectional PID control, resonance vibration and cooperation of a two-stage pump; a Kalman filtering algorithm is integrated, pressure, temperature and vibration data are fused, and vacuum pump power and a heating strategy are adjusted; and constructing a multi-index evaluation system, and driving strategy iterative optimization through a reward signal.

Provided in the present invention is an artificial-afforestation area extraction method based on a high-resolution remote-sensing image. The method comprises: acquiring a remote-sensing image, and preprocessing the remote-sensing image; inputting the preprocessed remote-sensing image into an intelligent forest extraction model, so as to obtain a distribution map for all forests; performing edge detection and Hough transform line detection on the distribution map for all the forests, so as to obtain artificial afforestation regions and natural forest regions; and calculating the areas of the artificial afforestation regions and the areas of the natural forest regions, so as to obtain statistical results for artificial afforestation and natural forests. The present invention can solve the shortcomings of conventional monitoring means in terms of efficiency, cost and accuracy, and provides technical supports for large-scale forestry investigation and ecological monitoring.

The invention relates to the technical field of remote sensing image processing, computer vision and three-dimensional modeling, discloses a monocular remote sensing image building three-dimensional automatic reconstruction method based on an improved UNet network and a multi-parameter coupling model, and aims to solve the problems that a traditional reconstruction method depends on DSM or laser radar data, and is poor in complex terrain adaptability and low in automation degree. According to the method, a single high-resolution remote sensing image serves as input, building and roof pixel-level segmentation is achieved through an improved UNet network fusing a channel-space-semantic three-mode CBAM attention mechanism and a resolution adaptive SMU activation function, and footprint regularization extraction is completed in combination with contour curvature constraint and Hough transform. And constructing an elevation inversion model based on the roof and footprint offset, the imaging geometric parameters and the terrain correction coefficient, and finally generating a three-dimensional white model through a CGA parametric modeling rule. Experimental verification shows that the average absolute error of the method is about 1.06 m (the mountain scene error is smaller than or equal to 1.2 m), the height error of more than 93% of buildings is smaller than 2 m, the method does not need to depend on additional auxiliary data, the process automation degree is high, the method is adaptive to complex terrain scenes, and the method can be directly connected with a smart city and a digital twin system.

The application discloses an OCR-based medical document intelligent identification method and system, relates to the technical field of document identification, and comprises the following steps: collecting a medical document image through a mobile terminal, generating a binary image based on the collected image through a U-Net combined with multi-scale feature fusion and an attention mechanism, and performing clipping; based on the clipped binary image, extracting text information through an OCR model, and extracting structured fields according to the layout rules and geometric distribution of the medical document; and performing deterministic rule judgment and risk assessment on the structured data. The application reduces the image calculation complexity through standard gray scale processing, improves the text region feature extraction accuracy through the U-Net structure combined with the CBAM attention mechanism, realizes effective fusion of multi-scale features and noise suppression in combination with the Attention Gate, realizes text direction correction in combination with the Hough transform, and improves the text detection robustness and recognition accuracy.

The invention discloses a visual image processing method based on an automatic calibration system, and belongs to the technical field of machine vision, and the method comprises the steps: image collection and input: collecting an image containing a target line, transmitting the image data to an image processing module, and completing image format standardization and data loading; preprocessing the image; extracting edge information of the preprocessed image by adopting a Canny edge detection algorithm, and retaining edge features of the target line; performing straight line detection on the edge image by adopting probability Hough transform, setting a line segment length threshold and a gap threshold, screening to obtain a target line, and extracting pixel coordinates of the target line; and converting a target line pixel coordinate into an actual offset distance, correcting by combining with a system operation distance measured by an encoder to obtain a true value of the measured distance, and outputting the true value. According to the method, the problems of low image processing precision, poor robustness and insufficient real-time performance in a traditional automatic calibration system are solved, and the automatic calibration system is promoted to develop towards high precision, high automation and high robustness.

The invention discloses a to-be-welded weld center track real-time labeling method based on bidirectional edge enhancement and geometric constraint fitting, and relates to the technical field of automatic auxiliary welding. The method comprises the following steps: performing directional gradient edge enhancement on a weld image, fusing with an original image, and inputting into a deep learning network to extract an edge; candidate line segments are obtained through Hough transform, and effective weld boundary pairs are screened out based on groove width and parallelism constraint; calculating midpoints of the boundary pairs to form a central point set, and performing cubic B-spline curve fitting to obtain a smooth trajectory; and finally, the fitting track and the real-time deviation between the fitting track and the welding gun are displayed in an augmented reality mode in an overlapping mode. According to the method, the robustness and accuracy of welding seam edge extraction under complex interference are remarkably improved through directional enhancement and geometric constraint, and accurate extraction and visual interaction of a welding seam center track are achieved through high-precision curve fitting and AR visualization.

The present application relates to the technical field of vehicle bottom detection, and particularly relates to a high-speed train vehicle bottom detection method and system based on images. The method comprises: obtaining an image to be detected containing a reference component; extracting the main structure line of the reference component through edge detection and Hough transformation, and calculating the average direction as the reference tilt angle; inputting the image into a target recognition network with an angle branch selection structure, activating a dedicated processing branch according to the interval to which the tilt angle belongs for recognition, and outputting a preliminary recognition result; and using a preset spatial topology model, combining the position and tilt angle of the reference component, performing dynamic coordinate transformation and spatial position verification on the preliminary result, and filtering out the results that do not conform to the constraints. That is, the scheme of the present application realizes angle self-adaptive recognition and spatial topology double verification, thereby effectively dealing with image tilt and background interference, and significantly improving the reliability of detection in complex environments.

The application discloses a high-precision moon tracking method based on image processing, which comprises the following steps: according to local GPS information, the height angle and the azimuth angle of the moon relative to the local position are calculated by using an astronomical algorithm and are sent to an execution module for pre-adjustment, so that the moon is preliminarily tracked; the moon target in the gray-scale image is recognized by using a Hough transform, the position of the moon center in the image and the moon radius are obtained, the weight of each pixel point in the image to the moon brightness is calculated, the weighted center is obtained, the accurate position of the moon brightness center is obtained, the azimuth deviation and the pitch angle deviation of the moon brightness center and the optical axis are calculated, and if the deviation data exceeds a preset threshold value, the deviation data is sent to the execution module for correction and adjustment. The moon image is collected in real time by using a CCD, the moon center and the radius are recognized by using an image processing method, the accurate position of the moon brightness center and the deviation of the moon brightness center and the optical axis are calculated by using parameters, the high-precision real-time tracking of the moon is realized, and the method has the advantages of high precision, rapid response and the like.

This invention discloses a video tracking and recognition method for the spatial trajectory of hoisted objects based on multi-source data fusion, belonging to the field of machine vision monitoring technology. The method includes acquiring pre-loaded physical boundary constants of the tower crane and simultaneously collecting multi-source data during tower crane operation. Based on the physical boundary constants, kinematic calculations are performed on the multi-source data to obtain the relative vertical displacement, the three-dimensional absolute spatial coordinates of the luffing trolley center point, and the absolute coordinates and three-dimensional rotation matrix of the camera's optical center in the global three-dimensional coordinate system. Edge detection is performed on the temporally aligned color video frame matrix to extract candidate wire rope contours and generate a binary edge pixel matrix. Collinear edges are mapped to polar coordinate space using Hough transform. This invention reduces the uncertainty of monocular vision depth scale and improves stability and accuracy under conditions of large-amplitude lifting and complex swinging.

The invention relates to the field of non-contact measurement, in particular to a coherent dispersion vibration measurement data correction method based on a camera calibration technology. The method comprises the following steps: converting a light energy signal of an interference fringe image into a digital signal, and outputting a gray value expression of the interference fringe image; if inclination occurs, constructing a mapping relation between the inclination amount of the interference fringes and the inclination angle of the interference fringes based on an image motion path principle; calculating the phase difference of the interference fringes before and after inclination, and constructing a mapping relation from the inclination amount of the interference fringes to the phase difference and then to a measurement result error; based on the built detector internal and external parameter model, forward mapping transformation is carried out on an original interference image, lens distortion and perspective errors are eliminated, and therefore the rotation angle theta of interference fringes is accurately extracted through Hough transformation on the basis of the geometrically accurate image; and reverse compensation correction is realized based on the mapping relation of the inclination amount of the interference fringes, the phase difference and the measurement result error and the rotation angle theta of the interference fringes. According to the invention, a vibration mode information measurement error caused by inclination of interference fringes is eliminated.

The application relates to the technical field of positioning methods, in particular to a positioning method and system for part machining, which comprises the following steps: acquiring a part image of a part to be machined and position parameters of a positioning feature on the part to be machined; constructing a kinematics prediction model based on the position parameters of the positioning feature, obtaining a velocity vector of the positioning feature at the next moment, and obtaining a predicted position of the positioning feature at the next moment based on the velocity vector; determining an adaptive search domain at the next moment based on the change degree of the motion state of the positioning feature; and performing local Hough transformation on the part image collected at the current moment within the adaptive search domain range to obtain the current position parameters of the positioning feature; the application realizes high-frequency and low-delay tracking of the part pose in the machining process, and improves the accuracy and response speed of dynamic compensation.

This invention provides a method for synchronous measurement and data fusion of surface coordinates, position degree, and step difference in a wind tunnel, belonging to the field of surface technology in wind tunnels. This invention acquires raw point clouds by deploying a laser displacement sensor array and a 3D scanner in the wind tunnel test section. A refraction field prediction model is established using temperature and pressure data, and optical path deviations are corrected using a ray tracing algorithm. The point cloud is divided into surface regions and step difference regions using octree spatial indexing and region growing segmentation. The position degree deviation is calculated using the moving least squares method for the surface regions, and sub-pixel-level step difference measurement is achieved for the step difference regions through morphological operations, double-threshold hysteresis tracking, and Hough transform. Finally, a multi-source data fusion weight allocation function is established based on measurement uncertainty to generate a comprehensive measurement report. This invention solves the technical problem of the inability to synchronously and accurately measure the three types of geometric parameters—surface coordinates, position degree, and step difference—under high-speed airflow conditions in wind tunnels.

This invention provides a method and system for extracting structured data from certificates and licenses, applied in the field of data processing technology. This application determines the layout by using the first 5 characters of the first line in the machine-readable area and features of the visually readable area. It dynamically records any unconfigured new layouts, generates new layout configurations and annotations, completes the extraction of structured data for the new layout, and updates the training dataset in real time. A three-level positioning algorithm fusion strategy is employed, using machine-readable area information as a reference to calculate the consistency score of surname and given name groups, the accuracy score of date data, the proportion of structured data acquisition, and the positioning point deviation value. A preset priority ranking is used, and Hough transform and SIFT feature matching are applied to process image deviations, generating a comprehensive positioning processing result. Using verified accurate data from the machine-readable and visually readable areas as a reference, abnormal certificate data is automatically corrected and completed to generate data to be detected. Based on a target dual-channel OCR model, the data to be detected is processed to generate target certificate and license structured data.

The invention provides a target detection model training method and a target detection method, and the training method comprises the steps: inputting a training sample with a to-be-detected target into a convolution fusion network of a Hough detection model, outputting a plurality of space fusion features, and enabling the training sample to have a corresponding Hough space label, the Hough space label is generated by processing a sample label of the training sample by using a Hough transform algorithm; inputting the plurality of spatial fusion features into a detection accumulation network of a Hough detection model so as to map the image spatial features to a discrete Hough space for target detection, and outputting prediction spatial information of a to-be-detected target, the prediction spatial information representing position size information of the to-be-detected target; and calculating a model updating gradient of the Hough detection model in the discrete Hough space based on the prediction space information and the Hough space label, mapping the model updating gradient to an image space to update model parameters of the Hough detection model, and obtaining a trained target detection model.

The application discloses a method for identifying the posture of a profiled steel based on Hough transformation. The method comprises the following steps: S1, performing image preprocessing on an input profiled steel section image; S2, performing edge detection on the profiled steel image and extracting a contour; S3, extracting a section contour of a target profiled steel according to a region center coordinate; S4, performing straight line detection on the section contour by adopting Hough transformation; and S5, judging the posture of the profiled steel according to the straight line slope of the section. By means of image processing and Hough transformation and other related technologies, the application constructs an automatic identification system for the posture of the profiled steel, realizes the identification of the posture of the profiled steel section on a profiled steel production line, marks the profiled steel with an abnormal posture, further realizes intelligent sorting of the profiled steel production line, improves the production efficiency of a steel enterprise, reduces the labor cost, and further promotes the intelligent manufacturing transformation and upgrading and the intelligent development of the steel metallurgy field in China.

The application discloses a kind of power line detection and identification method based on DP-TBD, it is characterized by comprising the following steps: step one, FFT: the signal obtained is processed by FFT;Step two, phase accumulation;Step three, power line tower detection: the position of all suspected power line tower targets is judged by double-threshold CFAR data processing;Step four, power line detection: form radar image by DP-TBD;Step five, power line identification: linear detection is carried out using Hough transform, and the candidate power line is extracted, and the true power line is identified using the Bragg scattering characteristics of power line.The application adopts the above-mentioned power line detection and identification method based on DP-TBD, can solve the problem of strong fluctuation target detection when processing actual power line echo data, and can eliminate the influence of clutter false alarm, give power line tracking results, and improve the detection and identification effect of power line.

This invention discloses a method, device, and storage medium for early warning of thermal runaway in energy storage systems, relating to the field of energy storage system monitoring technology. The method first employs a scheme where the control input signal changes over time within a preset signal range according to a preset signal variation rule, obtaining output signals corresponding to different input signals, thus acquiring richer parameter information. Subsequently, by constructing an output signal sequence, the aforementioned rich parameter information is simulated as image information, facilitating data analysis and processing. Finally, based on Hough transform processing of the output signal sequence, the characteristic structure in the parameter information can be efficiently and accurately identified. Based on this characteristic structure, the probability of thermal runaway is analyzed, thereby improving the accuracy of early warning of thermal runaway in energy storage systems.

The invention relates to a target identification and positioning method based on non-training type feature identification, which comprises the following steps of: pre-designing feature identification which comprises two types of distributed feature identification and clustered feature identification, and enabling one of the feature identification to be adsorbed or attached to a target object; acquiring an image containing the feature identifier and the target object by using an image acquisition device; performing preprocessing and edge detection on the image to obtain an edge image; based on Hough transform, circular features and linear features are identified from the edge image, and circular parameter information and rectangular parameter information are extracted; identifying the type of the feature identifier in the edge image through a preset discrimination condition; and executing a positioning calculation process based on the identified feature identification type. According to the invention, a training-free feature identification coding-decoding system is designed, the dependence degree of the system on pre-training data is reduced, and the real-time response capability is improved.

This invention relates to a dynamic scanning compensation method and DNA synthesis device based on inkjet printing, belonging to the field of DNA synthesis. By applying threshold segmentation, connected component analysis, and circular Hough transform to a standard analysis image, a circular region for each reaction site is obtained. Based on the distance of all edge points of each reaction site from the center of the circular boundary, it is determined whether each reaction site meets the roundness requirements, thus obtaining information on missing reaction sites. Simultaneously, to address potential inaccuracies after the circular Hough transform, the results are calibrated using the YOLO target detection model based on a convolutional neural network, achieving high-precision detection of missing reaction sites suitable for industrial applications. Furthermore, by adding recognition weights for defective reaction sites such as those with "partially excessive distances from the center" during YOLOv6 model training, the compatibility between the two is further improved, enhancing detection accuracy and meeting the requirements of high-precision DNA synthesis.

The application provides a kind of unmanned aerial vehicle attitude correction method and system based on visual feedback, it is related to unmanned aerial vehicle visual servo control technical field.The application obtains the original color image of fan blade after, based on HSV grass green threshold generation grass binary mask and carry out grass gray scale suppression, obtain grass suppression gray scale chart;Edge map is generated and is subjected to probability hough transform line segment detection, according to the angle distribution of line segment determines horizontal dominant mode or vertical dominant mode, respectively determines slope, intercept or inverse slope, inverse intercept and filters line segment, obtains blade center line equation by K-means clustering;Horizontal movement component and vertical movement component are calculated again, and are converted into unmanned aerial vehicle drift angle adjustment amount and gimbal pitch angle adjustment amount.The application can reduce the interference of complex background line segment, reduce the parameter instability caused by fixed slope expression under vertical imaging attitude.

The present application relates to a kind of suspension bridge pin joint type sling rotation angle measurement method, comprising the following steps: S1, selecting the two places of sling of suspension bridge around lower pin shaft rotation site are carried out transverse marking and longitudinal marking;S2, the marked part of rotation site is videoed and frame is extracted, and the image sequence after frame extraction is obtained;S3, the image gray scale of image sequence after frame extraction is carried out using self-defined single channel method;S4, after the pre-processing of gray-scale image, all straight lines of image are identified using Hough transform, and the long straight line located at the edge of transverse marking and longitudinal marking is extracted;S5, the angle between long straight line respectively located at the edge of transverse marking and longitudinal marking is calculated, and the angle value of sling around pin shaft rotation with time variation in acquisition time period is obtained.The method proposed in the present application can realize the measurement of sling around pin shaft rotation angle, and provide reliable data basis for sling rotation law and related damage mechanism research.

This invention discloses a deep learning-based intelligent detection and classification method for wafer defects, belonging to the field of computer vision technology. First, a multi-scale pyramid decomposition is performed on the original wafer grayscale image. Then, a Fast Fourier Transform is performed on each layer of the image in the frequency domain. An adaptive sliding window detector and Hough transform are used to accurately identify and locate frequency peaks representing periodic background. Next, an adaptive notch filter is constructed and applied to suppress these periodic background frequencies, and non-periodic defect signals are explicitly enhanced by calculating frequency domain residuals. Finally, the multi-scale processed defect enhancement residual maps are fused to generate the final defect enhancement map, which serves as the input to the deep learning detection network. This invention solves the problem in existing technologies where low-contrast micro-defects are easily obscured by the strong periodic background texture of the wafer, leading to low detection accuracy and poor model generalization ability.

The invention belongs to the technical field of three-dimensional image processing, and particularly provides an indoor environment vertical rod object automatic detection method, system and device based on three-dimensional point clouds and a storage medium, and the method comprises the steps: carrying out the preprocessing of collected indoor three-dimensional point cloud data, carrying out the layering processing of the preprocessed point cloud data, and obtaining a three-dimensional image; projecting each layered point cloud slice to an xoy plane to generate a corresponding BEV image, performing circle detection on the BEV image of each layer by adopting Hough transform based on the known diameter of the vertical rod object, determining the circle center position of a circle in each BEV image, fitting the circle center point set by adopting a linear fitting method, determining the central axis parameter of the vertical rod object, and determining the center axis of the vertical rod object. And the position and the shape of the vertical rod object in the three-dimensional space are further obtained. According to the method, the geometrical characteristics and known diameter information of the vertical rod object are fully utilized, so that an efficient and reliable recognition effect is achieved.

This invention provides a method and apparatus for measuring contact wire grooves based on machine vision technology. The method involves placing the measuring device below the contact wire groove to be measured, and the device automatically focusing. The measuring device acquires the original image of the contact wire groove and marks the Region of Interest (ROI) of the original image based on a trained YOLOv5-LiteSE contact wire groove recognition model. The edge of the contact wire groove in the ROI region is obtained in HSB format, and a Hough transform is performed on the obtained edge to obtain the linear equation of the contact wire groove to be measured. The angle values ​​of the two types of linear equations are calculated based on their slopes. The angle values ​​of the two types of linear equations are compared with preset standard angle values ​​for the contact wire groove to complete parallelism detection. The minimum distance between the two types of linear equations is calculated to complete the dimensional detection of the contact wire groove to be measured. This invention solves the problem of low measurement accuracy caused by the inability of existing machine vision measurement to achieve rapid automatic focusing when measuring contact wire grooves in tunnels.

The invention relates to the technical field of railway measurement, and discloses an unmanned aerial vehicle LiDAR-based existing railway line measurement method, which comprises the following steps: S1, ground target layout: designing square hard white paper targets of which diagonal lines are black lines, and arranging the targets according to different heights on two sides of a railway in an open area along the railway, adopting a total station to actually measure the coordinate and elevation of the center of each target; and S2, unmanned aerial vehicle LiDAR flight: unmanned aerial vehicle LiDAR equipment adapting to the measurement precision requirement of the existing railway line is selected, and flight operation is executed in combination with a ground base station erection scheme. According to the method, coordinate conversion parameters are established through target arrangement and measured data of a total station, a point cloud processing flow is optimized in combination with intelligent algorithms such as a Ransac algorithm, 2D Hough transform and regional growth, the measurement effect that the plane coordinate precision is smaller than or equal to 5 cm and the elevation precision is smaller than or equal to 2 cm is finally achieved, the actual line type of the existing railway line is precisely restored, and the accuracy of the existing railway line is improved. And the high-precision requirements of maintenance, reconstruction, upgrading and the like are met.

This invention relates to the field of infrastructure engineering technology, specifically to a machine vision-based method and system for locating spraying areas. The method includes: using a depth camera to capture the original point cloud of the beam surface, segmenting and selecting target planes on the beam surface using the RANSAC algorithm; converting the image to two dimensions, extracting line features using Hough transform and edge detection, inversely mapping back to three dimensions, and selecting the uppermost point of the line; moving the camera upwards to capture the point cloud of the upper part of the beam, removing interference to extract two target planes; calculating the normal vectors of the two planes, comparing the point cloud to obtain the coordinates of the calibration points, constructing a new three-dimensional coordinate system and generating a rotation matrix; and planning the robot spraying path based on the beam surface design parameters. The system includes: a depth camera, point cloud segmentation, two-dimensional mapping and feature extraction, three-dimensional coordinate recovery and key point selection, spatial calibration, and path planning modules, which work together to implement the above method, eliminate point cloud blind spots, improve positioning accuracy, and meet the requirements for precise spraying of beam surfaces.