416 results about "Imaging algorithm" patented technology

Disclosed in the present application is a chip hybrid simulation method based on general verification methodology and an image processing algorithm. The method comprises: upon receiving a chip simulation task, starting a verification platform; inputting excitation data into a design module under test, and performing simulation processing on a target chip, so as to obtain simulation output data; transmitting to an image processing model the simulation output data and the excitation data to be processed, and converting the formats thereof, so as to obtain a simulation data code stream and an excitation data code stream; processing the excitation data code stream, so as to obtain an image algorithm output data code stream; and comparing the simulation data code stream with the image algorithm output data code stream, so as to obtain a verification result. The present application solves the technical problem that chip simulation verification modes in the prior art isolate the interconnection and connectivity between image algorithm models and UVM simulation verification platforms, are thus prone to cause data errors and algorithm processing flow errors, and therefore reduce the simulation efficiency and accuracy of the UVM platforms.

The invention relates to the technical field of image design, and discloses an artificial intelligence-based image style design method and system, and the method comprises the steps: extracting a cue word initial field of an input text, generating a high-dimensional vector according to the cue word initial field, and carrying out the sampling in a probability distribution space according to the high-dimensional vector, and determining a prompt word supplementary field according to the occurrence probability of the predicted vocabulary, combining the prompt word initial field and the prompt word supplementary field to determine a preliminary text, performing repeatability analysis on the target text, and determining an arrangement sequence of each keyword of the target text. And training the multi-modal large language model by adopting a diffusion model and a mask algorithm, determining a target multi-modal large language model, determining a visual image of the target text based on the arrangement sequence and the target multi-modal large language model, performing image processing on the visual image by adopting a preset image algorithm, and determining a target visual image. The image of the image style is generated through artificial intelligence, and the reliability of cover design is improved.

The invention discloses an array type optical fiber ultrasonic imaging system based on multi-path delay parallel detection, and belongs to the technical field of ultrasonic detection and imaging. The main working process of the system is as follows: continuous laser output by a laser is modulated into pulse laser to be transmitted to an optical fiber ultrasonic detector array, and ultrasonic waves emitted by an ultrasonic wave generating device act on each detection array element to cause the property change of a detection optical signal; through multi-path time delay with the optical fiber as the medium, the detection optical signal forms different transmission time in each channel, is distinguished in a time division mode and is combined into a single path to be output. After photoelectric conversion, electric signals containing multi-channel detection information are collected in parallel, the signals are analyzed at a terminal processor by means of an imaging algorithm with an array parallel architecture, information of a to-be-detected target is obtained, and image reconstruction is completed. The array type optical fiber ultrasonic imaging system has the advantages of being accurate and rapid in scanning detection, simple and compact in system structure and efficient and high-quality in image reconstruction, and large-scale and low-cost array type optical fiber ultrasonic imaging is achieved.

The invention relates to a GIS (Geographic Information System) basin-type insulator defect detection method, electronic equipment and a storage medium, belongs to the technical field of intelligent image identification and processing of power equipment, and solves the problem of how to improve the accuracy of GIS basin-type insulator defect detection. The method effectively improves the resolution and information integrity of defect images, constructs a MobileNetV2 and YOLOv11n lightweight network structure for the technical bottlenecks of large FMC data volume and complex imaging calculation, achieves the intelligent closed-loop process from defect identification, positioning and classification to segmentation evaluation, achieves the automatic identification and geometric quantity extraction of defect areas, and improves the accuracy of the detection result. A data support is provided for subsequent early warning judgment and decision making; according to the method, the ultrasonic detection identification precision and the imaging quality are effectively improved.

The invention provides a high-frequency electromagnetic excitation cable detection image reconstruction method and system, and belongs to the technical field of underground facility multi-field coupling detection and imaging, and the method comprises the steps: applying high-frequency electromagnetic excitation to an underground medium; receiving an electromagnetic signal transmitted through an underground medium, synchronously collecting a sound signal generated by electromagnetic excitation, and preprocessing the electromagnetic signal and the sound signal; performing structure imaging on the electromagnetic signal by adopting a frequency wave number migration imaging algorithm to obtain medium structure distribution; performing electrical parameter imaging on the electromagnetic signal by adopting a Born approximate inverse scattering imaging algorithm to obtain medium electromagnetic characteristic parameter distribution; sound source distribution reconstruction is carried out on the sound signals through a time reversal algorithm, and medium sound source response characteristic distribution is obtained; and inputting imaging results of the three algorithms into a pre-constructed deep reinforcement learning model for dynamic fusion to generate an underground medium comprehensive parameter reconstruction image.

The invention discloses a method and a system for monitoring a dynamic burning state of a printed circuit board (PCB), and relates to the technical field of PCB burning process monitoring and image processing. And the coverage of the whole process from image acquisition, path analysis, contact state evaluation to electrical fluctuation monitoring is realized. According to the system, an image algorithm is combined with a crimping behavior modeling technology, mechanical anomalies such as probe crimping path offset, contact inconsistency and time sequence mismatch can be accurately identified, electrical anomalies such as micro-contact resistance change can be detected, and monitoring precision and real-time performance are enhanced. By constructing the abnormal risk time anchor point and accessing the block chain for evidence storage, data credibility and subsequent traceability are guaranteed, meanwhile, a locking and alarm mechanism is matched, automatic isolation and closed-loop management of equipment abnormity are achieved, and burning quality consistency, equipment operation stability and maintenance efficiency are effectively improved.

The invention discloses a pavement hidden disease nondestructive testing method and system, and the method comprises the steps: carrying out the alignment of electromagnetic wave, sound wave and thermal field signals through a space-time synchronization algorithm for a first signal set, and integrating multi-modal data through a weighted fusion model, so as to obtain a second fusion data set; for the first feature set, if the feature value is lower than a preset threshold value, the environmental noise interference is suppressed through an adaptive filter, and a second feature set is obtained; deep disease features are extracted from the second feature set, and a generative adversarial network is adopted to perform high-resolution reconstruction on small holes and crack extension to obtain a third feature set; and for the third feature set, generating a road deep structure image by adopting a three-dimensional imaging algorithm, and determining positions and forms of small holes and crack propagation through an image segmentation technology to obtain a first disease distribution diagram. According to the invention, the accuracy and efficiency of road deep disease detection can be effectively improved.

The invention discloses an image stabilization method and system based on inertial measurement data, and the method comprises the steps: obtaining video frame motion optical flow data in real time through a video collection device, obtaining video optical flow motion information through image algorithm processing, and recording and transmitting motion inertia data of a camera through an IMU (Inertial Measurement Unit); the two types of data are preprocessed, and then time axis alignment is completed; accurate video data motion information is obtained through bidirectional correction; kalman filtering is introduced for forward compensation, and a motion model is established to solve a rotation translation matrix; dividing the image into local pixel spaces, calculating a transformation matrix of each region, and obtaining a radiation transformation matrix by adopting neighborhood weighting; and finally, triangular region filling is realized through an OpenGL graphic rendering library, and a stable image is output. According to the method, IMU data and visual information are creatively fused, the real-time performance and accuracy of video image stabilization are remarkably improved through multi-level motion compensation and local optimization, and the method is particularly suitable for meeting the requirement for high-quality image stabilization in a motion scene.

The invention relates to the technical field of magnetic material injection molding, and discloses an injection molding magnetic powder orientation optimization method and system based on rheology-magnetic field coupling. The method comprises the following steps: deploying a distributed sensing network at key positions of a mold runner and a mold cavity, and synchronously capturing dynamic viscosity change gradient and magnetic field intensity space distribution of a melt in real time in a filling process. The captured data are input into a multi-physics field coupling simulation engine based on micromechanics, an instantaneous orientation vector field of the magnetic powder in the cavity is obtained through calculation, then an orientation order degree topological graph is constructed through tensor analysis and an image algorithm, and a weak orientation area and the boundary of the weak orientation area are accurately recognized. And finally generating a composite regulation and control instruction of the magnetic field and the injection parameters according to an identification result. According to the method, online diagnosis and active compensation regulation and control of the orientation state of the magnetic powder can be realized, and the orientation uniformity and consistency of the injection molding magnet are improved.

The invention discloses a cable trench fireproof cover plate performance detection device and method based on multi-band electromagnetic waves. The device comprises a microwave detection module, a terahertz imaging module, a high-precision two-dimensional mechanical scanning platform, a synchronous control and data fusion unit and a comprehensive data analysis platform. The microwave module is used for transmitting and receiving 1-10GHz electromagnetic waves to realize deep structure detection; the terahertz module works at a frequency band of 0.1-3 THz and is used for acquiring near-surface time-domain spectral information. And the scanning platform drives the modules to perform two-dimensional rasterization scanning on the surface of the cover plate. And the synchronous control and data fusion unit coordinates the time sequence action of each module to ensure the time-space consistency of data acquisition. And the comprehensive data analysis platform performs fusion processing on the multi-source data through an inversion and imaging algorithm to generate a comprehensive evaluation report covering internal macroscopic defects, near-surface microdefects and coating thickness. According to the invention, through collaborative integration of microwave and terahertz technologies, multi-scale and integrated nondestructive testing and performance evaluation of the fireproof cover plate from a deep layer to a surface layer are realized.

The invention discloses a self-adaptive underwater single-pixel imaging technology based on Fourier modulation, belongs to the technical field of computational optical imaging, is used for ocean detection and improvement of ocean surveillance and defense efficiency, and adopts a self-designed LED array to replace an expensive DMD and a four-quadrant detector composed of common photodiodes to replace an expensive barrel detector. And high-speed illumination and parallel acceptance are realized while the cost is greatly reduced. The technical route comprises a high-speed underwater single-pixel imaging device, a high-speed Fourier single-pixel imaging algorithm and an adaptive sampling strategy based on Fourier single-pixel imaging. The high-speed single-pixel imaging device comprises a self-designed LED array light modulation module, a converging lens module and a receiving module. The high-speed Fourier single-pixel imaging algorithm comprises a binarization algorithm based on error diffusion and an algorithm based on adaptive compressed sensing. According to the innovative self-adaptive sampling strategy based on Fourier single-pixel imaging, a specific sampling path is generated by fusing circular sampling and random sampling according to the characteristics of an image in a Fourier spectrum, so that Fourier coefficients which have great influence on image quality are acquired as much as possible under water with low sampling rate and high turbidity, and the image quality is improved. Therefore, the high-efficiency imaging of the underwater target object is realized, and the imaging speed and quality are both considered.

The invention discloses a satellite-borne SAR (Synthetic Aperture Radar) real-time imaging method based on FPGA (Field Programmable Gate Array) high-speed transposition, which comprises the following steps of: in FPGA data writing operation, carrying out row splitting on SAR echo data in a matrix form to obtain a plurality of small matrix blocks with consistent row and column sizes, and forming a recombination matrix; writing the small matrix blocks into a memory in sequence; performing skip reading operation on the recombination matrix, and repeating the operation until the reading operation of the SAR echo data is completed; imaging is carried out based on the CS imaging algorithm, real-time output is carried out, according to the method, the transposition operation read-write efficiency of a memory is high, and the imaging real-time performance is guaranteed.

The invention relates to a thick-wall pressure vessel inner wall damage detection method based on a low-frequency ultrasonic phase control imaging algorithm, which comprises the following steps: calculating to obtain an ultrasonic Lamb wave frequency dispersion curve of a thick-wall plate-shaped structure, and selecting a low-frequency guided wave frequency; determining parameter configuration of array elements of the phased array according to the low-frequency guided wave frequency, and arranging the array elements on the outer wall of the pressure vessel; exciting a low-frequency Lamb wave signal, and collecting FMC data through phased array elements, including echo signal data of each array element; frequency dispersion compensation processing is carried out on the acquired FMC data, and a phase coherence factor of each imaging pixel point is calculated; and adopting a TFM (Time Frequency Modulation) algorithm based on phase coherence factor weighting to carry out delay weighted reconstruction on the compensated FMC data to obtain an imaging and positioning result of the damage defect of the inner wall of the pressure vessel. Compared with the prior art, the problems of guided wave propagation frequency dispersion effect, complex mode, low signal-to-noise ratio and the like can be solved, and high-precision imaging positioning of inner wall damage defects is realized.

The invention discloses an imaging dynamic range expansion method based on variable integral time, and relates to the field of infrared imaging. The method comprises the following steps of: firstly, sending an infrared original image acquired by a medium-wave infrared movement to an FPGA (Field Programmable Gate Array) chip, decoding integral time of the infrared original image, and converting a signal format; then the integral time information and the converted infrared image signal are synchronously sent to a non-uniformity correction module, and non-uniformity noise is corrected; performing dynamic range expansion of the fused image signal on the corrected image signal through a variable integral time imaging algorithm; and finally, digitally displaying the obtained fused image signal. The core innovation of the method is that a hierarchical integral time segmentation calibration model is established, and a non-uniformity correction parameter dynamically matched with integral time is obtained. The method breaks through the physical limitation of single-frame integration time, the low-temperature background signal-to-noise ratio is remarkably improved while high-temperature target details are reserved, and the method is particularly suitable for the refined imaging requirement of a high-dynamic complex infrared scene.

The invention provides an unmanned aerial vehicle dynamic path planning method and system for a strong electromagnetic interference environment, and relates to the field of unmanned aerial vehicle navigation and interference resistance. The method mainly solves the problem of robust positioning of an optical sensing system in a strong electromagnetic environment. The core of the method lies in a spectrum anti-noise technology of a visual SLAM module, and spectrum separation of visible light and near-infrared band electromagnetic noise is realized through electromagnetic shielding packaging and an adaptive imaging algorithm of a multispectral camera. According to the method, a time-frequency double-domain filtering algorithm is adopted, the phenomena of light intensity abrupt change and frequency spectrum aliasing generated by electromagnetic pulses are eliminated in the feature extraction stage, and the electromagnetic interference mismatching rate of ORB feature points is reduced to 2% or below. The optimized feature point cloud keeps electromagnetic immune characteristics in the dynamic updating process, centimeter-level positioning precision can be achieved in the field intensity environment of 30 dBm or above, and meanwhile real-time detection of dynamic obstacles is achieved through an optical flow acceleration constraint mechanism.

The invention discloses an underwater robot imaging algorithm optimization method based on deep learning, and relates to the technical field of underwater robots, and the method comprises the steps: firstly carrying out the dynamic capture of an underwater light field through employing a curved surface CMOS array sensor, and adaptively adjusting the focal length of the curved surface CMOS array sensor according to the change of the light intensity of a scene, and activating the local pixels when the scene changes; designing a quantum convolution kernel, mapping classical convolution operation into a quantum circuit, constructing a hybrid calculation pipeline, and performing quantum state joint coding and cross-domain feature fusion on optical and acoustic data to obtain fused data; through quantum Fourier transform and quantum convolution, data of different modalities are fused, the accuracy and robustness of imaging are improved, weights of optical images and sonar data can be dynamically adjusted under different water turbidity and illumination conditions, the fusion effect is improved, model errors are reduced, metasurface parameters can be adjusted in real time, sub-wavelength wavefront regulation and control are achieved, and the method is suitable for large-scale popularization and application. And the adaptability and robustness of the imaging system are improved.

The invention relates to the technical field of candid photographing identification, and discloses a candid photographing equipment positioning and alarming method combined with ambient light perception. The method comprises the following steps: arranging a sensor array, a signal receiving node and an image acquisition device in a target area, acquiring real-time ambient light intensity and color distribution change data, and generating a change trend of the real-time ambient light intensity and color distribution change data; adopting statistical analysis to identify abnormal ambient light changes, and outputting coordinates and features of the abnormal light changes; corresponding images are obtained and preprocessed, secret photographing equipment features are extracted through an image algorithm, verification is carried out in combination with a rule base, and candidate positions are determined; accurate position coordinates are obtained through a triangulation positioning method; and if ambient light abnormity and candid shooting characteristics exist at the coordinate at the same time, the candid shooting device is judged, and the alarm system receives the coordinate and triggers alarm and interference measures. According to the invention, candid photographing identification precision and positioning capability in a complex scene are improved.

The invention relates to a medical image tuning method, and belongs to the technical field of image processing. According to the invention, a medical image tuning device is adopted; a core board fusion algorithm module automatically identifies the number and pixels of connected cameras, automatically matches a built-in image processing algorithm module, and performs preprocessing through an exposure algorithm module, a denoising algorithm module and a contrast enhancement algorithm module to obtain a single or multiple images as a state 1; extracting image features of the state 1, automatically selecting a single-image algorithm module or a multi-image fusion algorithm module, and obtaining a state 2 through the fusion algorithm module; low-delay and high-pixel transmission is met, image information is completely reserved, a camera host can better complete image processing, and the final image quality is guaranteed; the problem of image information loss caused by image processing at the camera module end is reduced, and the problems of module temperature rise, component damage, influence on imaging quality and the like caused by image processing at the camera module end are solved.

The invention belongs to the technical field of image processing, and discloses a three-dimensional radar sub-aperture imaging deformation monitoring device and method, and the device comprises a radar which is used for collecting echo signals passing through a preset position, processing the echo signals according to frequency mixing and analog-to-digital conversion, obtaining a three-dimensional echo matrix, and outputting the three-dimensional echo matrix; the three-dimensional echo matrix and a sampling ending signal are sent to a motion compensation module; the motion compensation module is used for resampling the azimuth direction of the three-dimensional echo matrix by adopting an interpolation algorithm; the distance matching filtering module is used for performing pulse compression on the distance direction of the three-dimensional echo matrix; the migration correction module is used for carrying out migration correction on the distance direction of the three-dimensional echo matrix after pulse compression; the sub-aperture imaging module is used for calculating a three-dimensional echo matrix after migration correction according to a sub-aperture imaging algorithm; and the deformation monitoring module is used for performing interference difference processing on the three-dimensional complex image to obtain deformation data. The imaging quality and the deformation monitoring precision can be improved.

The invention discloses an eye disease analysis method based on an eye image, and the method comprises the steps: carrying out the image segmentation of a sclera region through employing a convolutional neural network architecture u-net network model of image segmentation, carrying out the recognition of a hyperemia part of the sclera region, and calculating the proportion of the hyperemia part through employing FrangiFilter blood vessel recognition in combination with the color recognition of an LAB color space; by combining the image algorithm with a traditional image processing and deep learning model, the segmentation precision of the sclera region, the identification precision of sclera congestion, the robustness, the high accuracy and the rating accuracy are improved under the condition that the operation speed is not reduced.

The invention discloses a curvature compensation type metal material geometric dimension image measurement method and system, and relates to the technical field of dimension measurement. The curvature compensation type metal material geometric dimension image measurement method comprises the following steps: A1, sample dimension identification and outer contour test; a2, calculating and correcting the cross section area; a3, establishing a correlation model by considering influence factors; a laser measurement light curtain and a displacement sensor are combined to realize synchronous high-precision acquisition of the contour, the thickness and the length; a curvature compensation image algorithm is used for correcting measurement errors caused by curvature characteristics, and the measurement precision is improved; dimensional deviation, shape deviation and parallel section thickness and length verification are integrated, and it is ensured that the detection result is comprehensive and reliable; different sample expansion conditions caused by environmental factors and material factors are considered, a relation model is established, the sample expansion conditions can be directly predicted according to the environmental factors and the material factors, and then sample parameters are calculated more accurately; the whole process does not need manual intervention.

The invention provides a container positioning method, system and device based on a laser radar and a storage medium, and the method comprises the steps: obtaining first point cloud data covering a container and a loading and unloading platform, carrying out the iterative fitting of the first point cloud data through an RANSAC algorithm, obtaining plane equations corresponding to a plurality of dominant planes of the segmented container, and carrying out the calculation of the plane equations, calculating a current pose of the container in the reference coordinate system according to a plane equation of the plurality of dominant planes, and determining a first pose parameter; in the moving process of the container or the loading and unloading platform, second point cloud data obtained by scanning the tail environment of the container are obtained, local feature extraction and clustering filtering are conducted on the second point cloud data through an eight-neighborhood image algorithm to determine the tail position of the container, the first pose parameter is corrected according to the tail position of the container, and the tail position of the container is obtained. And outputting the second pose parameter of the container. According to the invention, multiple laser radars are matched, the real-time performance of scanning detection and the integrity of point cloud are ensured, and the precision and efficiency of container pose measurement are improved.

The invention provides an underwater robot submarine cable inspection motion control method based on an image algorithm, and belongs to the technical field of image processing, and the method specifically comprises the steps: determining an inspection processing mode of a robot according to the historical uninspected data of a submarine cable behind a current inspection position, and carrying out the operation control processing of the robot in the inspection processing mode, the method comprises the following steps: determining a secondary inspection review area by utilizing an inspection processing mode, and when the composition condition of the secondary inspection review area meets requirements, determining the secondary inspection review area needing inspection review on the basis of inspection data of the secondary inspection review area of various inspection processing modes before the current position, and the inspection power consumption in an area with a complex environment is reduced.

The invention discloses a squint synthetic aperture sonar imaging method based on a dynamic slant-range space-variant model, and relates to the technical field of synthetic aperture sonar imaging, and the method comprises the steps: constructing a dynamic slant-range model; inputting the converted data into the dynamic oblique variation model to obtain a residual space-variant distance bending amount, and compensating the space-variant distance bending amount by using a first filter; according to the dynamic slope distance model, expanding the azimuth time offset into an azimuth frequency modulation factor; based on the azimuth frequency modulation factor and the azimuth time offset, designing a second filter by using an improved extended nonlinear frequency modulation scaling algorithm, and filtering the corrected data by using the second filter; and carrying out Fourier transform on the filtered data, compressing the transformed data by using a third filter, and then carrying out inverse Fourier transform to obtain a focused synthetic aperture sonar image. According to the invention, the imaging performance and stability of the imaging algorithm in a low signal-to-noise ratio and strong multipath interference environment can be improved.

The invention belongs to the technical field of image classification and containers, and discloses an automatic image classification and packaging system and method based on a container technology. The system architecture comprises a business application layer, an interface interaction layer, an image algorithm layer, an engine framework layer, a resource management layer and a hardware base layer. According to the method, an image classification algorithm and a container construction arrangement technology are combined, limited computing power resources can be utilized, image classification tasks of different scales can be efficiently dealt with, image classification becomes easier to realize and manage, and image classification efficiency is greatly improved under the condition of extremely little manual intervention. Therefore, automatic image detection, recognition, classification and autonomous training, optimization and upgrading of a model algorithm can be carried out. According to the invention, the speed and accuracy of image classification are improved, the construction and deployment process of a program is simplified, the transplantation of a model algorithm is more stable and flexible, and the labor cost is reduced.

The invention discloses a method and a device for analyzing and correcting influence of phase errors between sub-bands of step-frequency ultra-wideband SAR (Synthetic Aperture Radar) data, and belongs to the technical field of synthetic aperture radar signal processing. The method comprises the following steps: carrying out amplitude error estimation and compensation on each sub-band echo signal; preliminarily estimating a phase error by using the partial overlapping characteristic of the sub-band spectrum; performing distance compression on each sub-band signal; establishing an optimization model based on a maximum norm, and solving an accurate phase error by taking the preliminarily estimated phase error as an iteration initial value; carrying out sub-band splicing and distance resolution compression on the sub-band signals after phase error compensation; and finally, a corrected SAR image is obtained through a CS imaging algorithm. According to the method, extra calibration signals are not needed, high-precision phase error correction can be achieved only based on echo data, the calculation efficiency is high, and the method is suitable for a high-resolution SF-SAR system.

The invention discloses a self-adaptive phase gradient self-focusing imaging method suitable for a strip SAR (Synthetic Aperture Radar), and belongs to the technical field of synthetic aperture radar signal processing. In order to solve the problems that a traditional phase gradient self-focusing PGA algorithm is difficult to be directly applied to a strip SAR due to multi-point interference and phase error space-variant characteristics, and the processing flow of the traditional phase gradient self-focusing PGA algorithm is not matched with that of a conventional RD imaging algorithm and the like, the invention provides a novel imaging compensation method. The method comprises the following steps: firstly, sequentially carrying out range-direction and azimuth-direction dechirp processing on SAR echo data, and carrying out Fourier transform to obtain a preliminary complex image, thereby fundamentally solving the application premise problem of a PGA algorithm; then, detecting and positioning strong scattering points in the complex image, and carrying out self-adaptive partitioning on the image according to the spatial distribution characteristics of the strong scattering points so as to effectively isolate mutual interference among different targets; then, independently applying a PGA algorithm to each sub-block, and accurately estimating and compensating a phase error introduced by non-ideal motion of the platform through an iteration mode; and finally, synthesizing the compensated sub-blocks into an SAR image which is clearly focused. According to the method, the estimation precision of the phase error is remarkably improved, and high-quality self-focusing imaging of the strip SAR data can be realized.

The invention discloses a large-view-field camera light calibration method and system based on polynomial fitting, and the method comprises the steps: setting a measurement target on a camera and a calibration plate rigid tool, building a fixed connection coordinate system, directly measuring the pose between the coordinate systems through employing a laser tracker and other external high-precision equipment, and achieving the decoupling of a pose error and an image algorithm. The method comprises the following steps of: acquiring a plurality of calibration plate images, performing global polynomial curved surface fitting on sparse feature angular points of each image, and establishing continuous analytic mapping from pixels to a three-dimensional space; and carrying out spatial straight line fitting on a plurality of three-dimensional points corresponding to the same pixel to obtain an accurate light equation of the pixel. Calibration results are stored based on a camera target seat coordinate system, and direct migration and use can be achieved only by re-measuring the conversion relation between the coordinate system and a world coordinate system during actual measurement. The method is suitable for any complex imaging system, and has the advantages of high precision, strong universality, good robustness, outstanding engineering practicability and the like.

The invention discloses a quarry slope radar monitoring system, relates to the technical field of quarry radar monitoring and processing, and aims to solve the problem of influence of dust and vibration on slope radar detection images by collecting dust information in air, dynamically adjusting radar transmitting power and enabling pre-corrected signals to enter an imaging algorithm. The method is advantaged in that image definition and identifiability are substantially improved, accurate image data are provided for subsequent geological analysis, safety monitoring and other work, vibration acceleration data are acquired, a piezoelectric ceramic micro-actuator is driven to cooperatively offset instant vibration, and the radar equipment can be effectively protected even under the condition that the radar equipment is influenced by vibration. And the generated slope radar image can still maintain relatively high geometric accuracy and phase accuracy, so that the image truthfully reflects the real form of the slope, and a reliable basis is provided for slope stability evaluation.

The invention relates to the field of camera calibration, in particular to a binocular camera positioning precision evaluation method and device, equipment and a medium. According to the method, the hardware dependence is extremely low, extra complex special equipment is not needed, only the binocular camera is used for collecting the calibration board image, the calibration board is a common tool for binocular system calibration, extra preparation is not needed, and the evaluation cost and the equipment threshold are reduced. And secondly, the operation process is simple, the process of constructing the X corner points through the image and screening the vertexes according to the number of the adjacent points can be automatically completed through a basic image algorithm, tedious manual intervention or complex modeling is not needed, and the steps are clear and efficient. And finally, precision quantification is direct and rapid, an evaluation result can be output based on comparison of geometric dimension parameters between vertexes and a true value, redundant data processing links are omitted, rapid quantification of positioning precision is realized, and the defects of complex equipment and tedious process of an existing method are thoroughly avoided.