654 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 intelligent operation sewage pipeline system and an implementation method thereof, and the intelligent operation sewage pipeline system comprises monitoring equipment, a cloud server, a GIS map model, a management platform and an operation management terminal which achieve data interaction through a 5G data network or the Internet. And the data is processed and analyzed by combining big data analysis, an intelligent algorithm and an image algorithm, so that prompting, decision making, renovation and evaluation of the defects of the sewage pipeline are realized. The specific implementation method comprises the steps of constructing the intelligent operation sewage pipeline system, arranging monitoring equipment, establishing a GIS map model, setting a management module, constructing functions of each module and the like. Effective scheduling of the sewage pipeline operation system and reasonable allocation of operation and maintenance resources are realized, and the efficiency and quality of sewage pipeline operation are improved. The problems of external water entry, sewage leakage, sewage overflow, road collapse and the like caused by sewage pipeline defects are solved.

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.

The invention discloses an intelligent road inspection method and equipment based on multi-dimensional vision fusion, and relates to the technical field of road inspection, and the method comprises the following steps: obtaining a foreground photo and a laser point cloud through a vision detection module, obtaining navigation data through a combined positioning module, and transmitting the navigation data to an industrial control module; performing uniform light enhancement on the foreground photo by using an image algorithm to obtain intermediate identification result data, and processing the intermediate identification result data in real time by using the laser point cloud after online motion compensation to obtain a pavement disease inspection result and an important highway facility target inspection result; and based on an important highway facility target inspection result, in combination with the intermediate identification result data and the corresponding laser point cloud, judging the missing and deformation damage of the facility target. According to the method, 2D image and 3D laser point cloud multi-dimensional information are fused, intelligent patrol of pavement diseases can be achieved, and the missing and deformation damage state of important highway facility targets can be automatically judged.

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 discloses a tunnel advanced geological forecast monitoring method and related equipment, and relates to the technical field of intelligent monitoring, and the method comprises the steps: obtaining the time domain response of a transient electromagnetic signal; processing the time domain response of the transient electromagnetic signal by using an adaptive filtering algorithm to obtain processing data; generating a three-dimensional geologic model according to the processing data by using a three-dimensional imaging algorithm; and predicting the geological condition based on an abnormal density function, a pattern recognition algorithm and the three-dimensional geological model.

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 handheld security check imaging method based on MEMS assistance, and aims to combine an MEMS sensor with an MIMO radar plate to form a handheld convenient security check imaging method with high precision and low cost according to the rapid and fine security check requirement of people in a security check place with dense people. Firstly, the radar board transmits a linear frequency modulation signal and receives an echo signal reflected by a target, and during the period, the MEMS sensor feeds back the motion track and position information of the handheld security check system in real time, and provides accurate attitude and position information. And secondly, position calibration is carried out on the track information of the MIMO radar antenna through the data of the MEMS sensor, and the zero drift of the accelerometer is compensated based on the wavelet packet change, so that the error caused by the system motion and the position change is reduced. And finally, residual phase errors are estimated and eliminated based on a particle swarm algorithm, non-contact three-dimensional imaging of the target is realized in combination with a wavenumber domain imaging algorithm on this basis, and hazardous article inspection is realized in combination with technologies such as deep learning and image recognition.

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 image data processing, in particular to a digital printing quality visual detection method and system for high-weather-resistance printed cloth, and the method comprises the steps: obtaining a surface image and a template image of the high-weather-resistance printed cloth, processing the surface image through an image algorithm, obtaining the color features of different channels, and carrying out the visual detection of the digital printing quality of the high-weather-resistance printed cloth. According to the color features of different channels, the surface image and the template image, determining the total difference value of each pixel value of pixel points at the same position of the surface image and the template image, and according to a preset local window and the total difference value of the pixel values, determining the possibility that each pixel point has color bleeding in a color layer in the preset local window, according to the method, the defect that the surface image is distorted and cannot highlight due to the influence of conditions such as light can be avoided, the potential bleeding area is determined according to the possibility so as to judge the digital printing quality of the high-weather-resistance printed cloth, and the accuracy of the potential bleeding area and the accuracy of quality detection are improved.

The invention relates to the technical field of radar digital signal processing, in particular to a radar echo frequency domain simulation and imaging system and method, a medium and equipment. The radar echo frequency domain simulation and imaging system comprises: a backscattering coefficient matrix generation module for calculating a backscattering coefficient matrix in combination with an earth surface digital map; the satellite orbit simulation module is used for calculating and obtaining satellite orbit azimuth sampling point coordinates by combining the satellite orbit and the coordinates of the point, closest to the scene center, of the satellite orbit; the spaceborne synthetic aperture radar echo rapid generation module obtains a spaceborne synthetic aperture radar baseband echo signal by using an inverse omega K echo generation method; and the spaceborne synthetic aperture radar imaging processing module is used for processing by adopting an omega K imaging algorithm to obtain a spaceborne synthetic aperture radar image. According to the method, the calculation complexity of generating the synthetic aperture radar echo signal in the extended scene can be reduced, and the efficiency and precision of synthetic aperture radar echo generation and imaging are improved.

According to the MIMO ground-based SAR, multiple-input multiple-output antennas are arranged in a certain array, and uniformly distributed virtual array apertures are formed to image a target area. Inconsistency exists between different transmitting channels and receiving channels under the influence of various factors, specifically, radar echoes received by virtual array elements have gain errors and phase errors, and the amplitude-phase errors need to be corrected. The invention provides an M IMO GB-SAR amplitude-phase error estimation method, starting from the improvement of an imaging algorithm, errors introduced by imaging are reduced at the beginning, then an amplitude-phase error model is established to carry out constraint solution on amplitude errors and phase errors, amplitude-phase error initial values are estimated according to a standard reflector, and the amplitude-phase error initial values are calculated. And a rapid and reliable initial value estimation method can be provided for subsequent fine correction.

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.