507 results about "Imaging algorithm" patented technology

The invention discloses a stainless steel surface defect detection method based on machine vision, and relates to the field of material defect detection.The stainless steel surface defect detection method comprises the steps that stainless steel surface images are collected, the collected multispectral surface images are subjected to image fusion, and a multispectral composite image is generated; performing detail enhancement on the low-resolution image through a super-resolution reconstruction algorithm to obtain a high-resolution image set; positioning a defect area position from the high-resolution image set through a defect detection algorithm, and obtaining a defect position data set; extracting a historical image defect feature set and a real-time image defect feature set from the defect position data set, and constructing a defect classification model according to the historical image defect feature set; inputting the real-time image defect feature set into a defect classification model, and performing defect classification and evaluation to obtain a defect classification data set; and generating and distributing a defect detection report. By combining the multispectral imaging algorithm, the super-resolution reconstruction algorithm and the defect classification model, the automation, precision and efficiency of stainless steel surface defect detection are improved.

The invention belongs to the technical field of optical precision measurement, and particularly relates to a wafer defect detection system based on dual-channel optical imaging. According to the invention, dark field scattering and diffraction phase microscopy are combined as two optical imaging channels; optical paths of the two imaging modes are compatible and synchronized; through optical system calibration and optimization, high-precision acquisition and alignment of multi-modal images are realized; calibrating the sensitivity and resolution of the system by using a standard sample to ensure that the detection performance meets a preset requirement; carrying out dual-channel synchronous imaging on the wafer sample to be detected, and respectively obtaining a dark field scattering image and a diffraction phase image; carrying out conjoint analysis on the multi-modal data through an image algorithm, extracting size, depth and structure information of defects, and generating a high-precision defect detection report; the system specifically comprises a dark field scattering imaging module, a diffraction phase microscopic imaging module and an image processing unit. According to the invention, the defect of a single imaging mode can be effectively overcome, and the detection precision and robustness of the system are remarkably improved.

The invention discloses a rock debris lithology analysis method based on a multilevel fusion image algorithm and X-ray diffraction. The method comprises the following steps: S1, acquiring rock debris image data under different resolutions; s2, generating a multi-level rock debris image feature matrix; s3, preliminarily generating a lithology classification result; s4, generating supplementary data of X-ray diffraction analysis; s5, fusing the supplementary data of the lithology analysis and the preliminarily generated lithology classification result, and finally generating a comprehensive lithology classification result; s6, the comprehensive lithology classification result is input into a dynamic adaptive analysis system, the dynamic adaptive analysis system adaptively adjusts an analysis strategy according to the rock debris sample complexity, and parameters of a dual-path convolutional neural network model and a multi-modal analysis algorithm are optimized through a real-time feedback and learning mechanism; and S7, outputting an analysis report containing mineral distribution, crystal structure and comprehensive lithology classification results. The classification accuracy of different types of rock debris samples is improved by about 15%.

The invention discloses an array type multichannel acoustic imaging device and an abnormal sound source positioning display method, and the device comprises a main microphone array which is used for collecting sound signals of a target region; the camera device is used for collecting an image of the target area; the thermal imaging device is used for collecting a thermal image of the target area; the first data processing and analyzing module is used for identifying an abnormal sound signal of the target area and preliminarily estimating the position of an abnormal sound source; acquiring sound field distribution of the abnormal sound source and generating a sound field image by using an acoustic imaging algorithm; the second data processing and analyzing module is used for inputting the change rate of the sound wave parameters of the unit area and the temperature into a second neural network model, obtaining the sound velocity of the unit area in the sound field image, calculating the temperature of the unit area, fusing the sound velocity of the unit area with the temperature of the corresponding unit area in thermal imaging and determining the abnormal temperature; and the display screen is used for displaying the sound field image marked with the abnormal temperature. The abnormal sound source can be effectively distinguished and positioned, and the abnormal temperature can be displayed.

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 belongs to the technical field of magnetic particle imaging, particularly relates to a stacked high-uniformity magnetic field free line magnetic particle imaging system and method, and aims to solve the problems that magnetic field free lines in an existing FFL-MPI are not uniform, imaging resolution is not uniform, and circumferential artifacts exist. The system comprises a laminated gradient coil unit composed of N groups of symmetrical coil pairs, each group comprising n layers of coils with increasing diameters, and a saddle-shaped driving scanning coil and a detection coil; through a reverse current mode of two adjacent layers of coils, the magnetic field amplitude is opposite to the current direction, the non-uniformity of the magnetic field is compensated, and a high-uniformity FFL gradient field is generated; the control system accurately collects and processes signals, the signals are fed back to the imaging unit after analog-to-digital conversion, amplification and filtering, and imaging is reconstructed through an imaging algorithm. According to the invention, the problems of non-uniform resolution and circumferential artifacts caused by non-uniform magnetic field are solved, and the imaging quality of FFL-MPI equipment is remarkably improved.

The invention provides an insulating part defect identification method based on a synthetic aperture ultrasonic imaging technology, which belongs to the technical field of ultrasonic defect identification, and comprises the following steps: step 01, setting parameters of an ultrasonic transducer to enable the ultrasonic transducer to emit ultrasonic waves, and attaching the ultrasonic transducer to the surface of a detected insulating part; 02, connecting the ultrasonic sensor with an acquisition card, and acquiring real-time echo data by using acquisition software; step 03, importing the data into data processing software for data processing, and performing defect imaging by applying an imaging algorithm; and step 04, performing defect analysis and positioning on the formed ultrasonic image. According to the method, the accuracy, reliability and practicability of defect detection of the insulating part are remarkably improved, and more effective technical support is provided for quality control in the industrial field. Due to the automatic and intelligent characteristics of the system, the detection efficiency is greatly improved, the uncertainty caused by human factors is reduced, and the system has remarkable practical value and popularization significance.

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 relates to a water supply pipeline health evaluation method based on an image algorithm, and aims to solve the problem that the health condition of a pipeline is difficult to comprehensively and quickly evaluate in the prior art. According to the invention, the image feature indexes are rapidly obtained by collecting the image data of the inner wall and the water quality of the pipeline and carrying out inner wall defect identification and water quality state detection on the pipeline by using an image identification algorithm. Meanwhile, a water supply pipeline health evaluation index system fusing pipeline environment, operation and historical data is constructed, comprehensive calculation and evaluation are carried out in combination with image feature indexes, and comprehensive and quantitative evaluation of the pipeline health level is achieved. According to the method, various defects of the pipeline can be effectively identified, the water quality condition is accurately evaluated, a scientific decision basis is provided for maintenance, repair and management of the water supply pipeline, the pipeline safety risk is effectively reduced, and the water supply safety is guaranteed.

The invention discloses a method for measuring the thickness of an oil film on the surface of a bearing bush and pressure distribution based on ultrasonic guided waves. The method comprises the following specific steps: step 1, embedding a piezoelectric plate array in the back of a to-be-measured bearing bush; step 2, acquiring a reference signal, changing oil film pressure and film thickness, and acquiring a guided wave signal; step 3, acquiring a noise signal, and performing trend noise fitting on the noise signal; 4, after trend noise fitting is completed, a segmented RMS index is established, and pressure and film thickness features are extracted; and 5, importing the extracted guided wave data into a convolutional neural network in deep learning to decouple coupling features, and imaging the surface of the bearing bush by using an RAPID imaging algorithm based on a probability damage imaging principle to complete measurement. According to the bearing bush surface oil film thickness and pressure distribution measuring method based on the ultrasonic guided waves, the thickness of the oil film can be accurately calculated, and the pressure of the oil film can be calculated.

The invention discloses a distributed intelligent metasurface cooperative communication and imaging integrated method, and belongs to the technical field of wireless communication and sensing. According to the method, a multi-antenna and multi-carrier frequency technology is adopted, and a plurality of reconfigurable intelligent metasurface RISs are utilized to cooperatively complete communication and imaging of a target region ROI. Firstly, an imaging target ROI is divided into a plurality of space grid units, and equivalent scattering coefficients of all the units form a vector to serve as a solving target. A plurality of RISs are installed at different positions of the ROI near field. Secondly, transmitting pilot signals for a plurality of times, and configuring different RIS phases; then, a receiving end obtains overall channel state information by using a channel estimation algorithm, and extracts channel components which finally reach a receiving antenna from a transmitting antenna through the ROI and the RIS in sequence; and finally, obtaining a sensing matrix, solving an equivalent scattering coefficient of the ROI unit by using a compressed sensing imaging algorithm, and completing target imaging.

The invention provides a crawler-type photovoltaic cleaning robot control method based on fuzzy logic and a proportional differential integral corrector, and relates to the field of robot control. A current course straight line is obtained through an image acquisition system in cooperation with an image algorithm, a current yaw angle error and a current transverse error are obtained, fuzzy reasoning is carried out on the yaw angle error and the transverse error to obtain a coefficient, and a control signal is obtained by combining two proportional differential integral correctors; and the control signal is subjected to hard constraint to obtain a correction value, and a motor control signal is generated to control a motor of the crawler-type photovoltaic cleaning robot so as to enable the crawler-type photovoltaic cleaning robot to run along the texture of the photovoltaic panel. Motion information can be obtained only through an image acquisition system, and the hardware structure is simple; the pose data obtained through an image algorithm is small in fluctuation, the crawler-type photovoltaic cleaning robot can be better controlled, and the cleaning effect on the photovoltaic panel is 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 an SAR (Synthetic Aperture Radar) interference suppression method based on intelligent image decomposition, which mainly solves the problem of poor SAR imaging interference suppression effect in a complex electromagnetic environment in the existing method. According to the implementation scheme, actually measured data are obtained, and interference signals are generated; respectively imaging measured data, interference signals and mixed data consisting of interference and measured data by using an imaging algorithm, and dividing into a training set and a test set; constructing an image decomposition interference suppression network comprising an image decomposition network and an interference suppression network, and carrying out iterative training on the network by utilizing the training set and adopting back propagation; and inputting the test set into the trained image decomposition interference suppression network to obtain an image after interference suppression. According to the method, interference suppression is carried out on the image level, sufficient interference suppression training can be carried out on a specific scene, the utilization rate of image data and the capability of suppressing interference of the specific scene are improved, and the method can be used for suppressing various interferences caused to radar imaging in a complex electromagnetic environment in target detection.

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 an image optimization method for an edge image algorithm. The method comprises the following steps: preprocessing a pre-acquired image; performing contrast enhancement and preliminary edge detection on the preprocessed image to obtain an edge-enhanced image; using a Canny algorithm and morphological operation to extract a significant edge in the image after edge enhancement, remove a pseudo edge and connect a fractured part, and obtaining an image after edge extraction; and optimizing the image after edge extraction by using the trained convolutional neural network model and a multi-scale feature fusion technology to obtain an image after edge optimization. Through image processing including noise reduction, graying, contrast enhancement, Canny edge detection and convolutional neural network optimization, noise interference in the image can be effectively removed, more significant and accurate edge features are extracted, and a high-quality edge detection effect can still be stably obtained even in a complex scene or a high-noise environment.

The invention provides a target translation compensation and three-dimensional imaging method and system for a close-range millimeter wave MIMO radar, and the method and system achieve the high-quality real-time three-dimensional imaging. The method comprises the following steps: receiving echo data and movement speed of a tested person, and carrying out phase calibration from multi-station sampling to single-station sampling on the echo data; performing two-dimensional Fourier transform and motion compensation in x and y directions on the echo data after phase calibration based on the motion speed of the tested person; performing two-dimensional inverse Fourier transform on the echo data after two-dimensional Fourier transform and operation compensation in the wave number domain # imgabs0 # and # imgabs1 # directions; performing coherent superposition on the echo data after the two-dimensional inverse Fourier transform to obtain echo data after motion compensation of the tested person; based on a static target three-dimensional imaging algorithm principle, processing the echo data after motion compensation of the tested person to obtain two-dimensional images of each distance direction plane; and performing traversal synthesis processing on the two-dimensional image in each distance direction plane to obtain a three-dimensional image of the tested person.

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 belongs to the field of artificial intelligence and human spine automatic surgery, and discloses a double-view spine pedicle screw implantation multi-scheme fusion auxiliary guiding method which comprises the following steps: shooting X-rays on a spine part of a patient to obtain a patient spine normal position map and a patient spine side position map; according to the spinal normal position map image and the spinal lateral position map image of the patient, a spinal normal position map segmentation model and a spinal lateral position map segmentation model are obtained; according to the regional difference and the position relation of the three key structures in the spine image, the puncture point, the puncture direction and the termination point of the pedicle screw implantation operation probe are solved through image algorithms such as edge detection, multilateral fitting and angular point detection; the probe is punctured from the puncture entering point to the termination point in the puncture entering direction, and the probe is detected and tracked in the operation process, so that doctors are assisted in achieving the automatic operation.

The invention discloses a step frequency continuous wave ground penetrating radar imaging device and a rapid imaging method, and belongs to the technical field of microwave ground penetrating imaging. The vector network analyzer is adopted, the radio frequency power amplifier is carried, the ultra-wideband directional antenna is combined, signals of a step frequency continuous wave system are adopted, the frequency range is 700 MHz to 3 GHz, the bandwidth is large, meanwhile, the large transmitting power can be achieved, and meanwhile the requirements for the detection depth and the detection resolution are met; according to the method, direct wave interference is eliminated by adopting a singular value decomposition algorithm, then the effective signal strength is improved based on an automatic gain algorithm, and finally clutter interference is eliminated by adopting a block matching algorithm, so that the signal-to-noise ratio is improved; according to the method, an F-K migration imaging algorithm is developed, processed data are converted to a frequency wavenumber domain through Fourier transform, wavenumber domain data are filled before fast inverse Fourier transform, and the transverse space sampling rate and the imaging speed are increased.

The present invention discloses a near-infrared visual displacement monitoring method and device based on an image algorithm, and relates to the technical field of visual displacement monitoring. The present invention reconstructs the contour points in the initial contour point set, calculates the weights of the reconstructed contour points, and performs fitting based on the weights to obtain a fitting circle of the initial near-infrared image, and then calculates the actual moving distance of the target based on the fitting circle. The present invention can eliminate discrete contour points generated by noise, occlusion or imaging errors by reconstructing the contour points, and obtain smoother and more consistent contour point data, so that the subsequent fitting circle calculation accuracy is higher; constructing the fitting circle based on the weights can effectively reduce the fitting deviation caused by noise or abnormal points, improve the accuracy and robustness of the fitting circle target positioning, and then improve the accuracy of solving the actual moving distance of the target, and improve the accuracy of near-infrared visual displacement monitoring.

The invention discloses a low-illumination image enhancement method based on learnable semantic prior, and the method comprises the steps: firstly dividing the input into two branches, taking a low-light visible light image as the input of the two branches, enabling one branch to be used for the restoration and reconstruction of image contents, and enabling the other branch to be used for predicting the semantic prior of the low-light image; in the first branch, performing enhancement processing on the input low-light visible light image; in the second branch, learning and predicting semantic prior information of the image; and gradually recovering details of the image by using semantic priori acquired from a semantic learning device, and finally generating an enhanced visible light image. And finally, designing a loss function to guide a training process of a low-light image algorithm. Concealed details in the low-light image are disclosed through the learnable semantic priori prediction task, so that the generation quality of the image is improved.