200 results about "Hough transforms" patented technology

The invention relates to the technical field of goods shelf detection, in particular to a self-detection method and device for goods shelf settlement, and provides the following scheme: obtaining a top view image through an image sensor arranged right above the top of a goods shelf, dividing the image into a plurality of grid units, and positioning a rectangular geometric shape by utilizing Hough transform; and screening a plurality of to-be-detected areas in combination with the edge features. For an area to be measured, homographic registration and ortho-rectification are carried out based on a reference image, a displacement field is obtained by adopting sub-pixel-level dense registration, and a geometric parallax component field corresponding to imaging parameters is obtained through robust estimation. And under the hypothesis of small deformation, inverting the parallax into a pixel normal distance, and carrying out weighted aggregation on the local region to obtain a local distance measurement result. And by iteratively combining adjacent grids, determining a settlement area boundary, and finally outputting a settlement detection result. Millimeter-level settlement quantification can be realized under a single-frame image, hardware transformation is avoided, and the method is suitable for automatic detection and long-term monitoring of multi-specification goods shelves.

The invention discloses a photovoltaic cleaning control method fusing RTK positioning and visual compensation, and belongs to the technical field of photovoltaic module cleaning. According to the invention, through the RTK track points acquired offline, the least square method is combined with the RANSAC algorithm to carry out segmented straight line fitting, and the original points are projected to the corresponding straight lines to realize collinear correction, so that the measurement error is eliminated, and a continuous and collimated navigation path can be generated; edge lines (photovoltaic module grid lines) with geometrical characteristics in the environment are detected in real time through a visual sensor, the line segment direction is extracted through Hough transform, the line segment direction is compared with the target course calculated through RTK, course deviation is estimated, dynamic correction is conducted through a control strategy, and it can be ensured that the photovoltaic cleaning robot operates along the expected path with high precision; and on the basis of a lightweight YOLOv7 model, the photovoltaic panel is subjected to pollution category division and mapped to optimal water pressure and brush speed parameters, cleaning parameters are dynamically modified, and then cleaning strategy control is completed.

The invention discloses a PDF (Portable Document Format) drawing recognition and information structured extraction method. The method comprises the following steps: generating a high-resolution bitmap through image preprocessing; positioning and classifying a text region, a table region and a symbol region in the drawing based on a target detection model of transfer learning; hough transform is combined with SIFT feature matching to identify engineering symbols, and sub-pixel positioning is realized through an RANSAC algorithm; after the oblique text is corrected through affine transformation, the content is extracted through OCR; reconstructing a table structure based on OPTICS clustering and projection analysis; constructing an RDF knowledge graph according to a coordinate association rule; and using U-Net difference to detect and position an omission area and complementing the omission area. According to the method, deep learning and image processing technologies are fused, the problems of low rotating text recognition rate, table structure loss and semantic association deficiency in a traditional method are solved, through lightweight model compression and TensorRT acceleration, the analysis accuracy is remarkably superior to that of the traditional method, and the method can be widely applied to the fields of constructional engineering, petrochemical engineering and the like and has wide application prospects. And the drawing information processing efficiency and the data integrity are improved.

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 discloses a tool fracture and fatigue intelligent detection method based on vibration signal analysis, and the method comprises the following steps: S1, installing a vibration sensor, and collecting the vibration signal of a tool in real time; s2, the collected tool vibration signals are preprocessed, and noise in the signals is removed; s3, performing time-frequency analysis on the preprocessed vibration signals, and extracting time-frequency features in the signals; s4, performing deep feature learning on the extracted time-frequency features to form deep features; s5, the depth features are classified and analyzed, and the health state of the cutter is output; s6, according to the health state optimization feature extraction and prediction result of the cutter, generating learning output; s7, evaluating the health state of the cutter in real time according to the learning output, and pushing alarm information; and S8, according to the alarm information, predicting the service life of the cutter and optimizing a cutter replacement and maintenance strategy. According to the method, short-time Fourier transform and Hough transform are combined, and the extreme learning machine is applied, so that intelligent detection on the fracture and fatigue of the cutter is realized.

The invention discloses an OCR-based medical document intelligent identification method and system, and relates to the technical field of document identification, and the method comprises the steps: collecting a medical document image through a mobile terminal, generating a binary image based on the collected image through U-Net in combination with multi-scale feature fusion and an attention mechanism, and cutting the binary image; based on the cut binary image, text information is extracted through an OCR model, and a structured field is extracted according to the typesetting rule and geometric distribution of the medical document; and performing deterministic rule judgment and risk assessment on the structured data. According to the method, the image calculation complexity is reduced through standard graying processing, the text region feature extraction precision is improved by fusing a U-Net structure of a CBAM attention mechanism, effective fusion and noise suppression of multi-scale features are realized in combination with Attention Gate, text direction correction is realized in combination with Hough transform, and the text detection robustness and recognition accuracy are improved.

The invention relates to the technical field of image data processing, in particular to an induction furnace coil temperature field visual monitoring method based on thermal imaging, and the method comprises the steps: obtaining the three-dimensional temperature field data of an induction furnace coil, and extracting high-temperature voxels; an adaptive weight is constructed for each high-temperature voxel, and the weight is composed of two parts: 1, the local linearity of the voxel is determined by calculating the projection density of the voxel in multiple directions; secondly, obtaining a colinearity score by calculating the consistency of the candidate voxel and other candidate voxels in the neighborhood of the candidate voxel in the local main direction; and performing weighted Hough transform on the candidate voxels to vote by taking the product of the two as an adaptive weight. Through the intelligent weighting mechanism, real linear fault signals can be effectively amplified, noise interference is suppressed, and the detection accuracy and robustness of early discontinuous linear high-temperature faults are improved.

A display video correction device includes: a feature extractor that extracts a feature from a camera image obtained from an in-vehicle camera; a Hough transformer that performs Hough transform on the feature extracted; a straight line detector that detects a plurality of straight lines in the camera image, based on a transform result of the Hough transform performed; a vanishing point calculator that calculates, based on the plurality of straight lines detected, first coordinates indicating coordinates of a vanishing point in the camera image; a difference calculator that calculates a difference between the first coordinates calculated and predetermined second coordinates; and a position corrector that corrects, based on the difference calculated, a position of a display area in the camera image to be displayed on a display device.

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 relates to the technical field of image processing, in particular to an automobile part cutting and positioning method based on machine vision, and the method comprises the steps: obtaining an image of a to-be-positioned blank of an automobile part, extracting a sub-contour, for each pixel point of the sub-contour, calculating the local form continuity according to the pixel point and adjacent pixel points at the two sides, and obtaining the local form continuity of the to-be-positioned blank of the automobile part; determining local gradient divergence according to gradient amplitudes and gradient directions of all pixel points of the pixel point in a local range, fusing the local form continuity and the local gradient divergence, determining the reliability degree of the pixel point, executing generalized Hough transform based on the reliability degree of all pixel points of all sub-contours, and obtaining the reliability degree of all pixel points of all sub-contours. And performing registration based on the positioning center and a pre-obtained standard component, and guiding cutting equipment to execute cutting operation. According to the method, the cutting accuracy of the to-be-positioned blank of the automobile part is improved.

The application belongs to the technical field of bridge intelligent construction, and specifically discloses a high-pier large-span continuous rigid-frame bridge pier settlement displacement monitoring method and system, which comprises the following steps: collecting image sequences at a main target of a pier and at a reference point sub-target under a main girder construction condition; processing the images, including low-illumination image enhancement and high-precision segmentation of optical marker points under a complex background; identifying the optical marker point center coordinates at the main target and the sub-target based on an adaptive threshold gradient Hough transform (ATGHT), extracting the displacement values at the main target of the pier and the reference point sub-target, and then compensating for the camera self-motion measurement error, so as to obtain the pier settlement value of the main girder in the cantilever construction process. The application utilizes a non-contact device to monitor the pier settlement displacement in the main girder construction stage, realizes non-contact, multi-measurement-point, high-precision and high-efficiency monitoring of the pier settlement displacement of the super-high-pier large-span continuous rigid-frame bridge under a complex background, and has the advantages of low cost, convenient use and flexible measurement point selection.

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.

The invention provides a visual celestial navigation method based on sun centroid extraction and sea-sky-line fitting. Belongs to the application of image processing technology in the field of celestial navigation. The method mainly comprises the steps of extracting a high-precision sea-sky-line by using a textural feature segmentation and Canny-Hough transform combined algorithm, and extracting a sky region based on a sea-sky-line segmentation result. A solar facula image candidate region and a sun candidate contour are constructed through a sky region, a dual optimization strategy of region similarity screening and facula feature scoring is proposed, and sub-pixel-level sun centroid coordinate calculation is realized by combining an intensity weighted center method and nonlinear least square two-dimensional Gaussian model fitting. And further constructing a camera coordinate system space vector through the orthogonal projection point of the sea-sky-line and the sun centroid, calculating a sun elevation angle based on a vector included angle, and performing angle compensation by adopting an atmospheric refraction correction model. And finally, fusing multi-moment observation data through a sun shift line positioning method, completing autonomous calculation of the ship position, and improving the positioning precision.

The invention discloses a method and a system for automatically testing a virtual cabin of a whole vehicle based on digital twinning, and relates to the technical field of vehicle testing, a three-dimensional working condition space containing an eye point coordinate set and a polarization angle set is constructed by simulating the height and head micro motion of a driver, and a head-up display image in the three-dimensional working condition space is acquired through a camera; constructing a correction coordinate system and a gray brightness mapping table by combining a standard calibration board, and converting the image into a physical brightness matrix; marking elements in the physical brightness matrix through a brightness threshold value, extracting two parallel straight lines of a main track and a ghosting track through Hough transform and ODR sub-pixel refitting, and calculating a visual angle difference and a ghosting brightness ratio; and generating a test result table containing the three-dimensional working condition index based on a double-threshold joint judgment rule. The system comprises an image acquisition module, a feature extraction module, a data calculation module and a test judgment module, realizes automatic and accurate quantitative automatic test based on the system, and improves the virtual cabin test efficiency and reliability.

An inspection and a method for the automated identification of an undesired deformation of an edge of a crossbeam of a shelf are disclosed, the method comprising the steps of: projecting, with a laser, a structured light pattern onto the inspection area, wherein the light pattern is aligned parallel to the inspection area and positioned in close proximity to the inspection area; capturing, with a camera, a raw image of the supporting element, which includes at least part of the inspection area and a reflection of the projected light pattern; generating an inspection image from the raw image by computer, wherein lines are extracted from the reflected light pattern of the raw image and converted into a line pattern and stored as the inspection image, and wherein straight lines are selected from the line pattern, in particular by means of a Hough transform;Computer-implemented verification, in the test image, of whether the selected lines include a broken straight line and / or a straight line with a different slope; and computer-implemented determination that the test area exhibits damage caused by deformation if the broken straight line and / or the straight line with the different slope is present.

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 optical fiber device precision machining, and discloses an aperture positioning method and system for optical fiber capillary chambering based on image recognition, and the method comprises the steps: obtaining multi-angle image data and process parameter data of an optical fiber capillary, and executing time synchronization and noise preprocessing; performing real-time extraction and enhancement on capillary aperture edge features by using a deep convolutional neural network and an edge enhancement algorithm; constructing a layered aperture recognition architecture, performing morphological optimization, and generating an accurate aperture contour; on the basis of a multi-parameter optimization algorithm of Hough transform and ellipse fitting, parameters of the aperture of the capillary tube are obtained through calculation; and an instruction is generated according to the capillary aperture parameter, and a laser reaming system is driven to execute precise reaming operation. The defects of irregular shape, large size fluctuation and the like of a traditional chambering process can be overcome, and the automation level and the production efficiency of chambering of the optical fiber capillary tube are greatly improved.

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 invention relates to the technical field of tunnel engineering construction, in particular to a calibration method of double-cross-mark positioning and punching equipment, which comprises the following steps of: mounting cross marks containing transverse and longitudinal marking lines at at least four positions of a to-be-punched area of a tunnel; installing a laser emitter and an image acquisition device on the drilling equipment, emitting laser and acquiring a gray scale image containing a cross mark and a light spot; after the image is preprocessed, a marking line and a light spot edge are extracted through edge detection, Hough transform and the like, coordinates of a cross mark center and a light spot center are calculated, and offset is obtained; and in combination with multi-sensor information, a self-adaptive control algorithm and PID control driving equipment are utilized to adjust displacement, and dynamic optimization is performed through monitoring distance until a light spot coincides with the center of the cross mark. The objective of the invention is to solve the technical problems of low calibration precision and low calibration efficiency of tunnel drilling equipment in a complex environment.

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 provides an X-ray imaging detection method and device. The method comprises the following steps: correcting original image data of X-rays to obtain a corrected original image, extracting high-frequency information of the corrected original image through Gaussian blur, and performing difference and noise suppression processing to obtain preprocessed noise reduction data; carrying out binarization processing on the preprocessed noise reduction data to obtain noise reduction data; performing edge processing on the noise reduction data through a Sober operator to obtain an edge image; and after the edge image and the corrected original image are fused, the line is identified through Hough transform, a detection part image is obtained, the device comprises a module for realizing the method, through the method and the device, the ray machine device in the DR detection system is optimized, a high-frequency constant-voltage ray machine is replaced by a conventional power frequency ray machine, and the equipment cost is reduced.

The invention relates to the technical field of molecular diagnosis and graphic processing, in particular to an electronic microarray chip dot matrix negative and positive judgment method based on an ISFET sensor. A chip hole site corresponds to an ISFET sensor, and after a hole bottom specific nucleic acid probe and a target sequence are hybridized and extended, the voltage of the sensor is changed. The method specifically comprises the following steps: data preprocessing: filtering median values of multi-frame electric signals of a hole site and taking a mean value; the dot matrix identification comprises the steps of threshold demarcation, Canny edge detection, circular Hough transform, optimal circle screening by Kdtree, array numbering and abnormal deletion processing to position a dot matrix; and calculating a result, extracting a signal difference value of the D1 stage and the D4 stage, evaluating numerical values of the positive quality control array and the to-be-detected sample array through a numerical value of the negative quality control array, and judging a final calculation result. According to the method, response changes are rapidly analyzed through image recognition, false detection and missing detection caused by numerical value differences in different arrays are reduced through multiple times of detection, and the detection accuracy is improved by selecting the most possible area through tickets.

The invention provides an SAR sidelobe signal reconnaissance method and system based on an FPGA. The method comprises the following steps: based on a block digital baseband signal, respectively carrying out frequency modulation slope estimation and local center frequency estimation processing on a target digital baseband signal by adopting short-time Fourier transform, binarization processing, parallel Hough transform and a K-Means clustering method, and correspondingly obtaining an approximate frequency modulation slope and an approximate local center frequency; performing matched filtering and CFAR detection processing on the target digital baseband signal based on the approximate frequency modulation slope and the approximate local center frequency to obtain a target preliminary judgment result; when the target preliminary judgment result is a high flag bit signal, performing iterative search on the approximate frequency modulation slope by adopting a matched filtering method and a one-dimensional search method to obtain a final frequency modulation slope; and obtaining the pulse repetition period of the target by using the final frequency modulation slope and the approximate local center frequency. The real-time processing capability is improved, and the reconnaissance precision is improved.

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 provides a subway train brake shoe thickness state abnormity identification method based on deep learning, and the method employs an RGBD camera to collect brake shoe image data, combines a semantic segmentation technology, precisely identifies the thickness of the thinnest part of a brake shoe, and judges whether the thickness accords with a standard range or not. Firstly, in order to improve the quality of an acquired image, multi-frame RGBD information is acquired from a brake shoe and fused; and sending the processed RGBD image into a brake shoe semantic segmentation network to obtain a segmentation mask of the brake shoe. Then, the circle center of the wheel is found through Hough transform, rays are radiated outwards based on the circle center of the wheel, and the line segment length of the rays in the segmentation mask is the thickness of the brake shoe at the position. The minimum value of the length set is the pixel distance of the thickness of the thinnest part of the brake shoe, and the actual physical thickness of the thickness of the thinnest part of the brake shoe can be calculated through the pixel coordinates and depth information of the two ends of the line segment. The inspection task can be automatically executed according to set requirements, and the stability and reliability of detection are improved.

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 invention relates to the technical field of structural plane trace extraction, and discloses a voxel morphology-based three-dimensional rock mass structural plane trace extraction method, which comprises the following steps of: performing point cloud normal vector estimation on rock mass three-dimensional point cloud by adopting robust random Hough transform, calculating a three-dimensional curved surface feature value of the point cloud through a normal vector neighborhood difference method, and extracting the three-dimensional curved surface feature value of the point cloud according to the three-dimensional curved surface feature value. Separating trace points from plane points by adopting a clustering method; carrying out voxel grid division on the point cloud space according to the point density, and carrying out expansion and corrosion morphological preprocessing on a voxel grid; a branch structure connection method is adopted to vectorize the voxel skeleton topology so as to complete connection and penetration of trace paths, and redundant branches and single rings are removed at the same time. According to the invention, the trace extraction effect and precision are improved.

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.