92 results about "Phase correlation" patented technology

The invention relates to the technical field of image processing, and discloses an infrared image enhancement method and system based on local phase correlation, and the method comprises the steps: obtaining a plurality of continuous frames of infrared images, carrying out the intelligent partitioning of a reference frame, and calculating the variance feature, the method comprises the following steps: selecting regions of interest with rich information, independently executing phase correlation operation in each region to extract a local translation vector, obtaining global displacement estimation through weighted fusion, adopting an abnormal value detection algorithm to improve robustness, and finally realizing sub-pixel-level image alignment and intelligent weighted fusion. The method is suitable for real-time enhancement processing of satellite-borne infrared remote sensing images, the resource constraint requirement of an embedded platform is met while the processing quality is guaranteed, and an efficient and reliable technical scheme is provided for space remote sensing image processing.

The invention provides an AGV navigation texture feature extraction and identification method, which comprises the following steps: analyzing the spatial distribution position of a plurality of extracted matching peak values according to the matching peak values, counting the number of the peak values and the repeated spacing between the peak values, and identifying a positioning fuzzy region caused by texture cycle repetition; scanning a local aperiodic mark position according to the identified positioning fuzzy region, and performing uniqueness verification based on the geometric shape feature and color attribute of the mark to obtain a unique anchor point coordinate and a mark type; according to the unique anchor point coordinate, combining with the tile texture data to extract a texture repetition interval, analyzing the size of the texture repetition interval and a preset threshold value, and determining a periodic intensity value; dynamically adjusting a position screening range according to the periodic intensity value, establishing a screening constraint condition, and applying the screening constraint condition to a phase correlation method to obtain a preliminary positioning candidate point set;

The invention discloses a rock and ore microscopic image splicing method and system based on deep learning, and relates to the field of image processing and the technical field of microscopes, and the method comprises the steps: obtaining a local rock and ore microscopic image of a rock and ore slice, carrying out the preprocessing of the local rock and ore microscopic image, and carrying out the overlapping region coarse registration of the preprocessed local rock and ore microscopic image through a phase correlation method; based on an improved image feature detection model, basic features and description features in the local rock and ore microscopic image after coarse registration are extracted, and local image features of the local rock and ore microscopic image are obtained; performing feature matching on the local image features of the two groups of local rock and mineral microscopic images by using an image feature matching model to obtain a matching corresponding relation of the local image features; and based on an image fusion algorithm of a homography matrix and a partial differential equation, splicing and optimizing the local rock and ore microscopic images in combination with a matching corresponding relation of local image features, and generating a large-view-field rock and ore microscopic image. According to the method, the complex transformation between the images can be better processed.

The invention relates to the technical field of image processing, and discloses a fluorescent lifetime microscopic image large-view-field splicing method and system and electronic equipment, and the method comprises the steps: constructing a tissue region mask for a to-be-spliced image, and constructing a vignetting model in a tissue region to complete vignetting correction; counting brightness indexes in an organization area to determine a global brightness reference, and realizing image group brightness unification through global zooming and single image brightness adaptive correction; further realizing alignment of adjacent images through feature point matching, and finishing image block splicing in combination with a minimum color difference suture fusion method to obtain an image band; an overlapping area is extracted from an image belt to generate an effective content mask, relative displacement is estimated by using a phase correlation method after low-frequency suppression, image belt splicing is completed by multiplexing a minimum color difference suture fusion method, a large-view-field spliced image is output, the automation degree and robustness of splicing are improved, the spliced image is geometrically consistent and visually seamless, and the splicing efficiency is improved. And the requirements of medical research on high-resolution and large-field-of-view fluorescence lifetime microscopic images are met.

The invention provides an automatic door and window quality inspection method and system based on machine vision. A thermal image sequence and pressure data are synchronously collected in the pressurization process, a heat conduction base line is established in the low-pressure stage, and heat conduction mutation caused by deformation of the sealing structure is captured in the high-pressure stage. Dynamic registration is carried out on thermal image data based on door and window geometric features to generate a displacement vector field, and abnormal heat flow vortexes and discontinuous boundaries are identified through a spatial-temporal feature extraction model. And starting a dynamic data compensation mechanism to generate a tracking atlas in a pressure attenuation stage, finally analyzing the phase correlation of an abnormal region in combination with a three-dimensional seepage model, outputting an air leakage path parameter, comparing the air leakage path parameter with a sealing failure threshold value, and generating a partition sealing performance grading evaluation result. According to the technical scheme provided by the invention, through thermodynamic dynamic modeling and multi-source data fusion, precise positioning of non-contact door and window sealing defects, three-dimensional seepage path reconstruction and quantitative leakage evaluation are realized, and the quality inspection precision and efficiency are remarkably improved.

The invention relates to the technical field of image recognition, and discloses a grassland degradation recognition method and system based on aerial images, and the method comprises the following steps: S101, obtaining a three-band reflectivity image; s102, extracting a red edge main direction and red edge anisotropy intensity; s103, performing screening to obtain a main response band-pass texture map; step S104, calculating to obtain a red edge texture coherence ratio index; step S105, marking the types of the pixels; and step S106, obtaining the area and proportion of each type of region. Structural features such as red edge main direction and anisotropic strength of a red edge form comparison graph are extracted, adaptive texture features are screened in combination with multi-scale filtering and main response scale, the optimal displacement vector is calculated through phase correlation to achieve space alignment, pseudo-coherent interference such as splicing seams and micro-displacement is effectively inhibited in cooperation with weighted covariance, and the method is suitable for large-scale and large-scale imaging. Therefore, grassland degradation identification precision is improved.

The invention relates to the technical field of jitter analysis, in particular to a high-speed oscilloscope eye diagram real-time jitter detection method and system, and the method comprises the following steps: carrying out high-speed collection waveform zero-cross detection to obtain a timestamp, calculating offset grouping to obtain a cross-period edge time sequence, merging a jump cluster according to a sequence hierarchy cluster, calculating offset, obtaining a drift trend vector, and calculating the jitter of the drift trend vector; and based on the trend vector distribution compensation weight, weighting and correcting the time coordinate, calling a corrected coordinate detection period boundary to calculate a difference value exceeding a threshold value, triggering correction to obtain a period correction rear edge coordinate, and extracting a jitter frequency amplitude according to corrected coordinate Fourier analysis to generate an eye pattern jitter detection result. Phase correlation is established based on a cross-period sequence, an analyzable offset mode is formed through a clustering identification distribution structure, accumulated deviation is suppressed through a drift trend and weight correction, an abnormal period is calibrated through period dynamic discrimination, and the relation between a jitter structure and amplitude is separated through frequency analysis, so that the overall stability and integrity are enhanced.

The invention relates to the field of optical precision detection, in particular to a sub-aperture splicing detection method based on a frequency domain and space domain cooperative strategy, which comprises the following steps: collecting discrete phase data of sub-apertures; constructing a continuous physical field model; performing coarse alignment on the adjacent sub-apertures by using a frequency domain phase correlation technology to obtain integer pixel displacement between the adjacent sub-apertures; performing sub-pixel fine alignment through a numerical gradient method based on a normalized cross-correlation function of a continuous physical field model and an embedded geometric error elimination operator; constructing a global robust energy functional to carry out global optimization; and solving the zero-rotation pose under the floating reference, and performing Gaussian gradient weighted fusion and adaptive orthogonal substrate surface shape reconstruction to obtain the full-aperture surface shape of the large-aperture optical element. According to the method, the problems of high initial value dependence, displacement-inclination degeneracy, accumulative error warping, abnormal value sensitivity and the like of a traditional sub-aperture splicing detection method are solved, and ultrahigh precision and high robustness of large-aperture optical element sub-aperture splicing detection are realized.

The invention discloses a solid tire quality detection method based on infrared and deep learning, and relates to the technical field of tire quality detection, and the method comprises the following steps: in the high-speed operation process of a solid tire, collecting a current infrared image, and extracting a high-gradient thermal texture reference point based on a phase correlation operator; and based on the extracted hot texture reference point, mapping a reference point track in a previous frame of image to the current image through adaptive optical flow tracking, and generating a hot texture original displacement vector field. According to the method, real inter-frame displacement identification is realized through hot texture reference point extraction and optical flow tracking, high-frequency disturbance is suppressed in combination with frequency domain filtering, image position reference is unified through weighted fusion and standardized offset mapping, and meanwhile, low-quality frame input is effectively judged and controlled by introducing continuous verification and dynamic weight regulation. The consistency and robustness of image splicing are integrally improved, the defect recognition error is remarkably reduced, and the detection stability and accuracy of the deep learning model in the industrial environment are guaranteed.

The application discloses a partial discharge diagnosis method based on phase correlation and phase statistical characteristics of ultrasonic signals, comprising the following steps: S01, based on the RLS algorithm, the collected ultrasonic signals are subjected to noise reduction processing to eliminate white noise, and Fourier method is used to filter out interference signals below 20 kHz; S02, while the step 1 operation is being performed, the starting point of the collected ultrasonic signals is found out; S03, the original signal is down-sampled to 500 data points by using the discrete Fourier transform, the frequency spectrum is windowed and then inverse transformed, the fuzzy entropy of the down-sampled signal is calculated, and a first diagnosis model is constructed; S04, the sampling points of the original data are reduced to 3600 data points in the form of taking the maximum value at a certain interval. The partial discharge diagnosis method based on phase correlation and phase statistical characteristics of ultrasonic signals can effectively determine whether the switch cabinet has a partial discharge phenomenon, effectively reduces the false positive rate, and can be used for online detection and daily inspection of electrical equipment such as switch cabinets in multiple scenes.

The application relates to the technical field of image processing, and discloses an infrared image enhancement method and system based on local phase correlation, wherein the infrared image enhancement method based on local phase correlation is used for acquiring continuous multiple infrared images, performing intelligent block division on a reference frame, calculating variance features, selecting an information-rich region of interest, independently performing phase correlation operation in each region to extract a local translation vector, obtaining global displacement estimation through weighted fusion, improving robustness by using an outlier detection algorithm, and finally realizing sub-pixel level image alignment and intelligent weighted fusion. The application is suitable for real-time enhancement processing of spaceborne infrared remote sensing images, can guarantee processing quality while meeting the resource constraint requirements of an embedded platform, and provides an efficient and reliable technical scheme for space remote sensing image processing.

The present application belongs to the technical field of radar imaging, and relates to a kind of dual-base radar cooperative imaging method based on complementary random waveform, it is characterized in that, including the following steps: step 1: establish dual radar cooperative detection echo model, radar 1 and radar 2 all adopt random frequency hopping waveform;Step 2: carry out distance difference compensation, eliminate the phase modulation term of the phase difference of the echo signal received by radar 1 and radar 2, realize distance difference compensation;Step 3: carry out echo motion compensation, eliminate the component related to velocity component in echo, realize echo motion compensation;Step 4: carry out band fusion imaging, rearrange different frequency band random frequency hopping, carry out zero filling processing to echo data on the missing frequency point, and through setting tight constraint condition, make the echo signal of two radar through FFT transformation to realize phase correlation fusion imaging.The imaging method of the present application designs new frequency hopping waveform, realizes efficient motion compensation, and realizes band fusion by using phase correlation processing algorithm.

This invention relates to a forward-looking sonar SLAM method and system based on deep Fourier registration. The method includes the following steps: S1: acquiring two sonar images; S2: performing feature extraction, Fourier transform, and Radon transform on the two sonar images to calculate their angular correlation curves; S3: performing rotation correction on the two sonar images and performing feature extraction and phase correlation calculation to obtain a phase correlation heatmap; S4: calculating the rotation variance and displacement variance of the two sonar images; S5: obtaining the covariance matrix by weighted correction of the variance based on the SSIM correlation of the two images; S6: constructing a noise model and explicitly specifying the noise model parameters for each observation factor. This invention introduces Fourier registration technology and deep learning technology into the front end of the SLAM system, enabling the SLAM system to effectively eliminate interference caused by complex seabed environments and better adapt to complex underwater environments.

According to the microscope time sequence scanning image splicing method based on global position optimization, relative displacement is calculated through a phase correlation method, global position optimization is carried out in a spatial dimension, and a global position parameter with the minimum error is obtained. And then aligning the image at each moment to the global optimal position of the first frame by using a feature point-based time sequence alignment method to realize the minimization of time sequence jitter. The method can eliminate accumulative errors, improve splicing precision and reduce artifacts in dynamic analysis, and is suitable for the field of microscope image processing and splicing.

The invention provides a Sigmoid modulation phase correlation surface wave full waveform inversion method and system. The method comprises the following steps: acquiring a surface wave seismic record and constructing an initial S wave velocity model; performing forward modeling to generate a synthetic record; calculating instantaneous phase correlation of observation and synthetic records to construct an amplitude-independent target function; modulating the target function by using a Sigmoid function, and mapping the value of the target function to a predetermined interval; and calculating a gradient based on the modulated target function and iteratively updating the speed model until a termination condition is met, and outputting a final model. According to the method, the smooth saturation and bounded output characteristics of the Sigmoid function are utilized, the target function response is converted into a self-adaptive gradient scaling mechanism, a self-stabilization optimizer is introduced for the inversion process, and the convergence domain is widened by applying smooth constraint, so that the dependence on the initial model precision is reduced, and the first success rate and the result reliability of inversion under complex conditions are improved.

This invention relates to the field of prefabricated building installation accuracy control technology, specifically disclosing an intelligent calibration system for prefabricated building installation accuracy that integrates point cloud data and BIM. The system collects on-site point clouds and acquires design geometric information; after distance attenuation compensation for the point cloud reflection intensity, it generates on-site and design projection intensity maps along the normal direction of the component installation plane, and obtains the in-plane pre-translation amount through phase correlation calculation; using this pre-translation amount as the prior center, it constructs a spatial transformation objective function containing an exponentially growing dynamic penalty coefficient, and solves the final spatial transformation parameters through weighted centroid closed-loop inversion iteration; it calculates the signed vertical distance along the normal of the aligned point cloud and removes outliers to form a deviation field, outputting translation or rotation fine-tuning amounts according to the median deviation of the joint sub-region; this invention solves the in-plane sliding problem in the registration of flat components, improving the accuracy of installation deviation calculation and calibration reliability.

The invention discloses a composite modulation signal blind identification method based on time-frequency analysis, which comprises the following steps of: firstly, judging and classifying input pulse signals, and then respectively carrying out identification and parameter estimation on determined signal types. According to the scheme, down-conversion analysis processing, signal time-frequency analysis processing and signal classification identification decision processing are firstly carried out on AD sampling intermediate frequency data sent by a digital signal processing board FPGA to obtain an identification result of a radar signal intra-pulse modulation type, and then corresponding parameter estimation is carried out according to the radar signal intra-pulse modulation type. According to two composite modulation styles of frequency coding and linear frequency modulation combination and frequency coding and two-phase coding combination, on the basis of time-frequency parameter characteristics of various composite modulations, related characteristic parameters are accurately extracted, a modulation type identification process is constructed, and time-frequency analysis is performed on radar signals through fast Fourier transform and phase correlation, so that the identification accuracy of the radar signals is improved. And furthermore, intra-pulse parameter estimation is accurately carried out on the radar signal.

The present application relates to the technical field of jitter analysis, in particular to a high-speed oscilloscope eye diagram real-time jitter detection method and system, comprising the following steps: high-speed acquisition waveform zero-crossing detection obtains timestamp calculation offset, grouping obtains cross-period edge time sequence, according to sequence hierarchical clustering merges jump cluster, calculates deviation, obtains drift trend vector, based on trend vector, assigns compensation weight, weightedly corrects time coordinate, calls corrected coordinate to detect period boundary, calculates difference, triggers correction when difference exceeds threshold, obtains period-corrected edge coordinate, and according to correction coordinate, Fourier analysis is carried out to extract jitter frequency and amplitude to generate eye diagram jitter detection result, in the present application, phase correlation is established based on cross-period sequence, and a resolvable offset mode is formed by identifying the distribution structure through clustering, the cumulative deviation is suppressed through drift trend and weight correction, the abnormal period is calibrated through period dynamic discrimination, and the jitter structure and amplitude relationship are separated through frequency analysis, so that the overall stability and integrity are strengthened.

The application provides an infrastructure surface crack feature enhancement method, medium and system, and belongs to the technical field of crack detection.The application obtains a corrected three-dimensional depth image by starting a line structured light sensing device and an inertial navigation device to scan an infrastructure surface, synchronously triggers a high-speed camera to collect a corresponding two-dimensional gray image by using an incremental encoder, establishes a stereo calibration model, and combines a sub-pixel level phase correlation algorithm to realize pixel level accurate alignment of the two-dimensional gray image and the three-dimensional depth image, extracts crack contour features and crack center lines, superimposes and fuses the crack contour features and the crack center lines to the three-dimensional depth image to generate a crack feature enhancement image, and inputs a multi-scale crack recognition model to recognize and classify, so that the technical problem that two-dimensional image texture features and three-dimensional depth data are difficult to realize pixel level accurate fusion, resulting in poor infrastructure surface crack feature enhancement effect, is solved.

This invention discloses a method and system for synchronous monitoring of bridge structural displacement based on wireless intelligent vision, belonging to the field of bridge health monitoring technology. The method involves acquiring images of the bridge monitoring area and performing format standardization processing; synchronizing the clock and frame acquisition timing of multiple monitoring nodes, achieving microsecond-level synchronization through linear drift compensation, hardware-triggered synchronization, and frame buffering technology; calculating sub-pixel displacement data using either target-independent or improved marker tracking modes, the former based on frequency domain phase correlation analysis, and the latter using a constrained attitude decomposition algorithm to improve accuracy; performing localized visual calculations on the displacement data and outputting displacement time-series data; and wirelessly transmitting the data to achieve real-time visualization and data interaction with downstream systems. The system includes modules for image acquisition, node synchronization, displacement calculation, local calculation, wireless transmission, and data interaction. This invention achieves non-contact, high-precision displacement monitoring, improves multi-node synchronization and data real-time performance, and is adaptable to various inspection scenarios.

The application discloses an interferometric SAR synchronization method of radar inter-satellite link combined with Ka-band link cross correction, and aims at the problems of interrupting normal SAR imaging or existing initial phase winding in the existing synchronization link method, and proposes a cross correction scheme of X-band radar inter-satellite link combined with Ka-band high-precision synchronization link. The system adopts a common reference source to ensure the phase correlation, and different synchronization time sequences are designed, that is, the X-band link is synchronized at a low frequency to eliminate the initial phase winding, and the Ka-band link is synchronized at a high frequency same as the SAR pulse repetition frequency (PRF) to realize high-precision phase measurement. During ground processing, the initial phase error of the Ka-band compensation function is corrected by using the X-band synchronization data, so that a high-precision phase synchronization compensation function is obtained, and the echo of the secondary satellite is compensated. The application solves the problem of initial phase winding under the premise of not interrupting the normal SAR imaging time sequence, and significantly improves the phase synchronization precision, image quality and absolute height measurement reliability of the dual-satellite interferometric SAR system.

The invention discloses an alternating current filter defect detection method, which relates to the technical field of image analysis, and comprises the following steps: constructing a one-dimensional current signal into a time-frequency image through time-frequency transformation, and introducing a visual model containing a self-attention mechanism to carry out global representation learning. Superposition features of concurrent defects can be naturally separated in a time-frequency two-dimensional domain, and compared with a traditional method only depending on a time domain or frequency domain rule, feature decoupling and difference amplification are easier to achieve; defect types, defect occurrence time periods and phase / branch serial numbers are output at the same time through a multi-task framework, so that classification and positioning are subjected to collaborative convergence in a unified network, and error accumulation caused by first classification and then positioning or multi-model series connection is avoided; multi-channel input is formed by three phases and unbalance, utilization of inter-phase correlation and asymmetric clues is enhanced, source phases can be distinguished, and abrupt change direction confusion is reduced.

The application discloses a high-speed optical layer-cut three-dimensional structure light illumination microscopic imaging method. The application solves a phase correlation coefficient, obtains an initial three-dimensional super-resolution image by using an original image, and then performs optimization to obtain a final super-resolution image according to the optimized super-resolution image, a wide-field image and a three-dimensional optical layer-cut image. The application combines spatial domain reconstruction and optical layer-cut, improves the speed of traditional 3DSIM reconstruction by 2-3 orders of magnitude, greatly suppresses the reconstruction artifacts caused by defocus background, and has good reconstruction capability on low signal-to-noise ratio data. The application can greatly reduce the operation cost, has the potential of real-time imaging and high-throughput imaging, has obvious advantages in reconstruction speed, fidelity and thick sample reconstruction compared with other algorithms, and greatly promotes the application of 3DSIM in large field of view and real-time imaging.

The embodiment of the invention relates to an image cross-modal saliency detection method and device, equipment and a medium. The method comprises the steps that a color image and a depth image corresponding to the color image are acquired; determining a first phase diagram corresponding to the color image, and determining a second phase diagram corresponding to the depth image; performing saliency phase updating processing based on preset distribution according to the color image, the depth image, the first phase diagram and the second phase diagram to obtain a saliency phase diagram; and according to the color image and the depth image, carrying out conversion from a frequency domain to a space domain on the saliency phase diagram to obtain a saliency map. According to the embodiment of the invention, on the basis that the discovered color image and the depth image have phase consistency of frequency spectrums with the saliency map, that is, the phase-related data obeys the preset distribution, the two modes of color and depth are taken as input, the saliency map is taken as output, and three different image modes are involved; and cross-modal saliency target detection with interpretability and relatively high accuracy is realized.

The application provides an anti-interference pipeline angle deviation detection method and device, and belongs to the technical field of underground pipeline detection. The method comprises the following steps: acquiring electromagnetic field X, Y and Z axis induction signals and corresponding sampling time information; extracting axis amplitude and phase parameters based on signal sequences, calculating amplitude correlation coefficient, phase correlation factor and amplitude stability evaluation parameters; obtaining an initial angle deviation reference value through XY axis amplitude parameters, correcting the amplitude correlation coefficient and weighting and fusing the stability parameters to obtain a final angle deviation reference value; obtaining a precise phase angle difference by combining XY axis phase parameters and the phase correlation factor; calculating the confidence degree based on the Z axis amplitude parameters to determine the effectiveness, and if the effectiveness is valid, the direction is determined according to the quadrant division rule, and if the effectiveness is invalid, the above process is repeated. The method integrates the three-axis signal characteristics, anti-interference is realized through multiple rounds of correction and effectiveness verification, the directional ambiguity is solved, and the detection accuracy and stability are improved.

The application discloses a multi-height lensless holographic microscopic image registration method based on frequency domain and space domain combination. Firstly, a Fourier phase correlation method is used to find a correlation peak value to coarsely calibrate a space translation error; then, for the coarsely corrected hologram, a method for quickly and accurately searching a matching feature point in a space domain is provided to obtain an accurate scaling factor, an image morphological technology is used, and the sample itself is used as the feature point, so that any marking on the sample is not needed; after the image scaling calibration is completed, the Fourier phase correlation method is used again to perform accurate translation calibration. Compared with the existing registration algorithm, the application can more accurately and quickly register and align the multi-height lensless holographic microscopic images.

The present invention relates to the field of image processing. Disclosed are a neural network-based pose estimation and registration method for heterogeneous images. In the present invention, a phase correlation algorithm is optimized to be differentiable and embedded into an end-to-end learning network framework, and a neural network-based pose estimation method for heterogeneous images is constructed. According to the method, an optimal feature extractor can be found for a result of image matching, a solution can be obtained without exhaustive evaluation, and good interpretability and generalization capability are achieved. The test results show that the present invention allows for accurate pose estimation and registration for heterogeneous images and shortening of the required time, has high accuracy and real-time performance, can meet actual application requirements, and can be applied in fields such as robot self-positioning.

The application provides a method for determining single singular point vortex phase topological charge and singular point position. Step one: a variable size non-circular symmetric diaphragm and a 4f system are added at the rear end of a to-be-measured light field; step two: a camera is moved along an optical axis to collect two light intensity images, and an axial differential of the light intensity is calculated; step three: a singular point position is determined based on a phase correlation iteration method; and step four: a vortex phase topological charge is iteratively calculated. The application can reduce the complexity of a traditional system for determining singular point vortex phase topological charge and singular point position, reduce the introduction of phase errors, and reduce system cost. In addition, the application only needs to collect two near-field light intensity images to determine single singular point vortex phase topological charge and singular point position.

The application discloses a kind of real-time vehicle speed measurement system and method based on vehicle side image, including step 1, camera real-time obtains vehicle side image;Step 2, calibration board is placed on the lane in front of camera to calibrate, and perspective transformation matrix is obtained;Step 3, the pixel displacement of vehicle between two frames is obtained using phase correlation method and optical flow method to the vehicle side image in step 1;Step 4, the position of tire in vehicle image is identified using YOLOv8 model to the vehicle side image in step 1;Step 5, according to the pixel displacement in step 3 and the position of tire in step 4, the actual moving distance between two frames of vehicle is obtained using perspective transformation matrix, and the real-time speed of vehicle is obtained.Through the frame displacement detection of vehicle side image when vehicle passes, the real-time speed of vehicle can be obtained, and the device cost is low, easy to install and maintain.

The application discloses a radar signal pulse repetition time blind estimation method and device and a storage medium. With the aid of the characteristics of optimal phase correlation accumulation data, the application can adaptively solve the PRT of pulses and the start and end time of data under ultra-low signal-to-noise ratio and is not limited by waveforms. In addition, the application can keep the data dimension from expanding and can effectively adapt to parallel acceleration calculation. The application groups, corrects and accumulates the data pulse string to be processed, uses the signal-to-noise ratio after accumulation, average accumulation efficiency and energy concentration to design a target function to search for an optimal grouping length, finally solves the PRT of pulses, improves the adaptability of electronic reconnaissance technology in a non-cooperative and low signal-to-noise ratio environment, provides strong data enhancement support for subsequent target radiation source fine intra-pulse modulation feature recognition and extraction technology, and can be widely applied to the technical field of computers.