16 results about "Hough transform" patented technology

The application provides an image recognition-based online detection method and system for surface defects of ultra-wide and thick plates, and relates to the technical field of image recognition. The method comprises the following steps: synchronously collecting surface images and process state parameters containing final rolling temperature, rolling force distribution, laminar cooling intensity and real-time speed, firstly completing image splicing according to the final rolling temperature and the rolling force distribution to obtain a topological image; then separating a texture channel image by feature decoupling with the laminar cooling intensity as a constraint; subsequently extracting edge features of the texture channel image by Hough transformation, and introducing the rolling force distribution and the real-time speed as constraints for curve fitting to generate topological structure features; finally dynamically adjusting a kernel function by a support vector machine, and analyzing the topological structure features and a preset feature library to obtain a defect detection result. The application can effectively improve the accuracy and adaptability of defect detection under complex working conditions through deep fusion and dynamic guidance of process parameters.

The application relates to an attention scheme and a strip convolution semantic line detection method based on a deep Hough network, and belongs to the field of image target recognition. The method comprises the following steps: adjusting the picture size of a data set, inputting a pretreated picture into a convolution model to perform feature extraction, attention mechanism and multi-scale feature fusion, and obtaining an output feature result. Different scale features are subjected to Hough transformation and fused feature regression prediction to obtain a recognition result; a strip convolution replaces a previous spatial convolution layer to obtain remote related semantic information of an input image recognition target; a strip-shaped pooling layer and a spatial pooling layer mixed pooling layer are used for pooling of a picture of a forward network; a channel attention module is added to a feature pyramid network to help modeling the importance of picture partitioning, so that the influence of redundant information of an input image on a recognition result is reduced; and a Ghost convolution light-weight network is designed to reduce the calculation amount of the network. Compared with a conventional semantic line detection method, the model parameter amount is smaller, and the recognition accuracy is higher.

The invention provides a field core box corner extraction and perspective distortion correction method based on multi-scale Retinex fusion. The field core box corner extraction and perspective distortion correction method comprises the following steps: S1, preprocessing a core box image based on anisotropic diffusion and multi-scale Retinex fusion; s2, core box angular point extraction based on topological constraint Shii-Tomasi and graph cut optimization interaction; s3, perspective correction based on thin plate spline transformation and Markov random field fusion; and S4, performing correction result precision verification and closed-loop feedback based on Hough transform straight line fitting and geometric moment invariant fusion. According to the method, accurate extraction of core box angular points and efficient distortion correction of images are realized, and meanwhile, full-dimension precision verification of correction results, adaptive migration of processing parameters of batch images and lightweight field deployment of algorithms are completed.

The invention provides a license structured data extraction method and system, and is applied to the technical field of data processing. According to the method, the format is determined through the first five characters of the first row of the machine reading area and the features of the visual reading area, the unconfigured new format is dynamically recorded, new format configuration and annotation are generated, new format structured data extraction is completed, and a training data set is updated in real time; a three-level positioning algorithm fusion strategy is adopted, a surname and name grouping consistency score, a date data accuracy score, a structured data acquisition proportion and a positioning point deviation value are calculated by taking machine-readable area information as a reference, a preset priority is adopted for sorting, meanwhile, Hough transform and SIFT feature matching are applied to process image deviation, and a positioning processing comprehensive result is generated; taking the verified accurate data of the machine reading area and the visual reading area as reference, automatically correcting and complementing the abnormal certificate data, and generating to-be-detected data; and processing the to-be-detected data based on the target dual-channel OCR model to generate target license structured data.

Advanced Hough-Based On-Device Document Localization. In an embodiment, lines are detected in an input image of a document. The lines are searched for candidate quadrilaterals. For at least a subset of the found candidate quadrilaterals, a contour score is calculated, and the candidate quadrilaterals are saved or discarded based on their contour scores. For each saved candidate quadrilateral, a contrast score is calculated. A final candidate quadrilateral is selected, based on the combined contour and contrast scores for the saved candidate quadrilaterals, to represent the borders of the document.

The application provides a method for judging the change of stored grain based on image processing. The application uses the local grain depression information of different time periods to compare the depression profile range, thereby avoiding the misjudgment caused by the change of stored grain to a certain extent. The method can achieve the purpose of automatically detecting the stored grain condition regularly. The application comprises a grain loading line detection module and a local grain surface detection module. First, the grain loading line detection module performs image preprocessing on the collected grain warehouse image, and then extracts the grain loading line by using Hough transformation, and cuts the image based on the line. Then, the local grain surface detection module performs edge detection on the cut image, and extracts the profile of the grain surface depression. Finally, the stored grain change detection module compares the extracted depression profile with the previous profile to judge whether the grain loading amount of the grain warehouse changes. The application can improve the accuracy of judging the change of stored grain.

The invention discloses a digital processing method and system for Zeeman effect experiment data, and relates to the technical field of atomic physics experiment data processing, and the method comprises the following steps: S1, image reading and preprocessing: obtaining a Zeeman effect interference image, carrying out the self-adaptive zooming according to the image size, converting the Zeeman effect interference image into a gray image, and carrying out the self-adaptive zooming according to the image size; enhancing the contrast ratio through self-adaptive histogram equalization, and removing salt and pepper noise by adopting 3 * 3 median filtering; s2, interference ring identification and measurement: carrying out edge detection by adopting a Sobel operator, detecting an interference ring in the image through Hough transform, screening two maximum concentric circles closest to the center of the image, automatically calculating the diameter of the ring, and converting the diameter of the ring into a physical size; s3, key physical quantity calculation: calculating a wavenumber difference, substituting the wavenumber difference into the optimized formula to calculate an electron charge-to-mass ratio, and performing error propagation analysis. Through automatic image processing and a precise algorithm, manual measurement errors are avoided, and the relative error of the electron charge-to-mass ratio can be reduced to 1.07% or below.

This invention discloses a positioning device for tensile testing of fiber-reinforced composite materials under cryogenic conditions, comprising a specimen positioning system, a refrigeration system, and a specimen perpendicularity adjustment system. The refrigeration system includes modules for overall environmental refrigeration, test zone refrigeration, and specimen refrigeration. The specimen positioning system includes a first tensile clamp and a second tensile clamp. The specimen perpendicularity adjustment system includes a suction cup, a multi-degree-of-freedom robotic arm, a gripper, an industrial camera, and an image processing system. The rotation angle calculation includes steps such as binarization processing, edge detection, and Hough transform. After determining the specimen angle and position, the internal telescopic shaft of the tensile clamp extends, pushing the gripper to move and clamp the specimen. Finally, different refrigeration methods are selected according to different experimental requirements to perform tensile testing of the fiber-reinforced composite materials. This invention, through a positioning device for tensile testing of fiber-reinforced composite materials under cryogenic conditions, achieves multi-stage refrigeration of the test environment and automatic specimen replacement and perpendicularity adjustment.

The present application relates to the technical field of image recognition and industrial vision detection, and discloses an electronic atomizer sensing signal data reading method based on image recognition, which comprises the following steps: collecting panoramic images through an industrial camera array and simultaneously locating LED indicator lights and tester screens by using a double-class YOLO detection network; estimating and compensating the interference of LED halos on screen areas based on a point spread function; unsupervisedly analyzing screen layouts through multi-scale Canny edge detection and Hough transformation; respectively performing OCR recognition and skeleton extraction on screen numerical reading areas and waveform areas; after adaptive brightness normalization and optical crosstalk compensation are performed on LED sub-images, extracting timing code matching sensing signals; and finally cross- verifying double-source data and outputting batch reading results.

The invention relates to the technical field of image analysis, in particular to an inorganic mineral casting defect visual detection method and system.The method comprises the following steps that extreme point coordinates are generated through two-channel gray level image non-overlapping scanning, direction angles of adjacent points are calculated through Hough transform to form an offset sequence, and a disturbance unit is marked through two-channel direction angle offset operation; connected domain analysis is combined with path length and disturbance quantity to determine a defect area, and principal axis equidistant sampling is carried out to construct a gradient spectrogram matching frequency fluctuation output defect type and coordinate. According to the method, the microscopic texture capturing capability is enhanced through two-channel gray level image non-overlapping scanning, structure distortion is quantized through a Hough transform direction offset sequence, illumination interference misjudgment is reduced through two-channel direction angle synchronous operation, and noise interference is restrained through connected domain two-index constraint judgment. The principal axis equidistant sampling is combined with gradient spectrogram frequency matching to realize accurate defect classification, and the detection precision and the anti-interference performance are improved by spatial arrangement feature quantization and multi-dimensional feature fusion.

The invention provides a main cable erection quality defect online monitoring method and system based on image analysis, and belongs to the technical field of suspension bridge construction quality control. Carrying out defect target positioning on the image with the defect in the preprocessed image by utilizing a YOLO deep learning model, then determining a twisting angle in the image with the defect by utilizing Hough transform, determining a surface damage area in the image with the defect by utilizing a region growing algorithm, judging a twisting condition according to the twisting angle, and determining a surface damage area in the image with the defect. The surface damage condition is judged according to the surface damage area, so that online quality monitoring is carried out in the cable strand erecting process; the technical problems that in the prior art, quality inspection is conducted in a manual inspection mode, the defect recognition error is large, and efficiency is low are solved.

The present application relates to pressure gauge metrological verification technical field, especially provide a kind of for pressure gauge identification method and device of test.The method includes the image of double pressure gauge of digital pressure gauge and pointer pressure gauge is collected using single camera, and image segmentation is carried out;The image of pointer pressure gauge is preprocessed;By constructing the character recognition model based on PaddleOCR library, the range of pointer pressure gauge and digital pressure indication are identified;The image of pointer pressure gauge is subjected to Hough circle transformation detection and straight line Hough transformation detection, and the scale line ring and pointer are obtained by setting threshold;According to scale line ring, after the mask of annular scale line non-scale area is constructed, polar coordinate is developed;The indication of pointer pressure gauge is calculated, the method overcomes the uncertainty and artificial error of manual reading, realizes the synchronous identification of double pressure indication, and improves the accuracy and efficiency of identification.

The invention relates to the technical field of metallographic analysis and grain size detection of metal materials, in particular to a twin boundary detection and elimination method for intelligent grading of grain sizes, and aims to solve the problem of deviation of an automatic grading result of the grain sizes due to the fact that twin boundaries generated after materials such as austenitic stainless steel or copper alloy are etched are easily misjudged as normal grain boundaries. According to the method, precise detection of the twin boundaries is realized through Hough transform, image restoration after twin boundaries are removed is completed in combination with a texture synthesis technology based on a Criminisi algorithm, interference of the twin boundaries on intelligent grain size rating is effectively solved, the processed metallographic image can be directly used for subsequent automatic grain size calculation, and rating precision is remarkably improved.

This invention discloses a non-contact measurement method and system for the spacing between rope and net nodes using a multi-sensor combination, relating to the field of big data processing. The method includes: establishing a multi-source measurement benchmark by integrating a binocular camera, LiDAR, infrared thermal imager, and tilt sensor; collecting spatiotemporally aligned data and fusing feature extraction, spatial registration, and a voting mechanism to achieve preliminary positioning of the rope and net nodes; obtaining the three-dimensional coordinates of the nodes through weighted least squares by combining sub-pixel coordinates, point cloud transformation, and quaternion pose compensation; constructing the rope and net topology using K-nearest neighbors, Hough transform, and graph theory, and calculating the initial spacing after selecting paired nodes; evaluating the standard deviation based on multiple sets of data, quantifying environmental errors, and using neural network compensation to obtain the measurement results of the rope and net node spacing. The advantages of this invention are: achieving non-contact, high-precision measurement of rope and net node spacing through multi-sensor fusion, spatiotemporal synchronous calibration, and intelligent multi-feature matching, while also possessing high anti-interference capability and scene adaptability.

The invention discloses a DEM rapid updating method and system based on minimum elevation difference mosaic, and belongs to the field of DEM dynamic updating, and the method comprises the steps: obtaining and processing natural resource monitoring data and regional historical DEM data; dEM change areas are extracted based on land type change rules, and pattern spots meeting area requirements are screened to serve as to-be-updated areas; generating a point cloud through dense matching of aerial triangulation achievements of the aerial image, and making a new DEM of a change area through processing; plane matching of the new DEM and the historical DEM is achieved through a SiftGPU, Hough transform and a UMEYAMA algorithm; setting a buffer area for a change range, calculating an elevation difference weight, generating an optimal mosaic line by using a minimum cost path algorithm, and implementing two-stage DEM seamless mosaic through feathering; and verifying the updated DEM precision based on field actual measurement points. The method and the system do not need a large amount of manual editing, and can improve the DEM updating efficiency.

The invention relates to the technical field of medical image processing, in particular to a CBCT image-based root canal system three-dimensional reconstruction and isthmus region identification method and system, and the method comprises the steps: obtaining CBCT original image data; the CBCT image is preprocessed; performing root canal system three-dimensional reconstruction on the preprocessed CBCT image; carrying out clustering segmentation based on a differential geometry theory on a root canal cross section in a three-dimensional reconstruction result of the root canal system so as to extract isthmus region features; the clustering segmentation based on the differential geometry theory comprises curvature flow driven super voxel segmentation, ellipse fitting and long axis extraction on a Riemannian manifold, and isthmus region feature extraction combining a geodesic distance field and Hough transformation; according to the method, the differential geometry theory is introduced, so that accurate identification of the root canal isthmus region is realized, the identification accuracy is improved, and the success rate of root canal treatment is remarkably improved.