1945 results about "Imaging technology" patented technology

The invention discloses a brain tumor multi-mode large model construction method, device and equipment and a storage medium, and is applied to the technical field of brain tumor imagines.The method comprises the steps that pixel-concept level alignment is conducted on a multi-mode MRI image and a pathological text; constructing a multi-modal feature fusion network for fusing image features and text features by adopting an attention mechanism of pathology perception and combining medical semantic information; training the multi-modal feature fusion network to generate an analysis report and a segmentation result; according to the technical scheme of multi-task cooperation, cross-modal pathological semantic accurate alignment, pathological knowledge graph injection and lightweight and continuous optimization parallelization, full-process coverage of brain tumor accurate segmentation, analysis report generation and prognosis prediction is achieved, the problems that a traditional model lacks pathological semantic support and is insufficient in clinical adaptability are solved, and the clinical adaptability of the traditional model is improved. And the deployment feasibility and the dynamic optimization capability are also considered.

The invention belongs to the technical field of industrial detection, and discloses a defect detection method and system based on honeycomb catalyst stacking. Omnibearing image data of honeycomb catalyst stacking are obtained through a multi-angle polarization imaging technology, pixel-level polarization degree parameters are calculated to construct a global polarization feature map, and accurate distinguishing between an intrinsic porous structure and suspected defects is achieved. A blind area identification and virtual view angle reconstruction mechanism is introduced, so that the problem of a stacked edge detection blind area is solved; and a layered reflectivity compensation function is adopted, so that the optical interference of an interlayer overlapping region is eliminated. Texture features are extracted through multi-scale morphological filtering, multi-dimensional feature fusion is carried out in combination with polarization features, edge continuity indexes and correction reflection intensity, and a high-precision defect discrimination model is established. And for a low-confidence region, dynamically adjusting detection parameters and performing iterative optimization to form an adaptive detection closed loop. According to the invention, the detection precision and reliability are improved, and the defect position, type and severity can be accurately output.

The invention relates to the technical field of three-dimensional imaging, in particular to a four-eye structured light stereoscopic vision imaging method, which comprises the following steps of: arranging four cameras and synchronously acquiring multi-view image data with structured light stripes; carrying out image preprocessing and stripe code identification on the obtained four-view-angle structured light image data, and extracting structured light stripe center line positions and corresponding space projection information under each view angle; generating three-dimensional point cloud data of the high-precision target sample by using parallax calculation and a three-dimensional reconstruction algorithm, and completing spatial registration and filtering optimization of point cloud; fusing the point cloud and the light intensity data by combining the reflection intensity information of the multi-view structured light stripes to generate a composite data set containing space and spectral information; and constructing a dense parallax field and executing three-dimensional consistency verification, and generating a high-fidelity three-dimensional reconstruction model with a complete topological relation and sparse shielding compensation capability. According to the invention, the problems of insufficient view angle coverage, shielding area information loss, difficult edge structure matching and the like of traditional stereoscopic vision imaging can be solved.

The invention relates to the field of power transmission line forest fire monitoring, and discloses a power transmission line multi-mode fusion intelligent forest fire monitoring method which is used for constructing an all-dimensional power transmission line forest fire prevention monitoring system and remarkably improving the intelligent level of power transmission line forest fire risk prevention and control. Comprising the steps of reasoning and identifying a smoke and fire target for a fused feature set by utilizing an improved YOLOv8 algorithm model, realizing accurate positioning of a fire point by adopting a haze penetration thermal imaging technology and a smoke cooperative monitoring technology in combination with spatial positioning, and establishing a temperature and humidity and forest fire occurrence probability correlation model based on historical data. According to the real-time temperature and humidity parameter dynamic adjustment algorithm model, the confidence threshold and the sensor acquisition frequency are detected, a dynamic threshold control method is adopted, an early warning threshold is adaptively adjusted according to parameters such as environment illumination, a final early warning result is output after cross validation of a multi-modal data fusion strategy, and the safety of the power transmission line is effectively guaranteed.

The invention discloses a server temperature control method and system based on deep learning, and belongs to the technical field of server temperature control, and the method comprises the steps: firstly, synchronously collecting server hardware temperature, business load and environment parameters, capturing a heat conduction relation through combining a thermal imaging technology, and constructing a dynamic relation network through time-space fusion processing; a heat flow prediction model is constructed based on the network, model parameters are dynamically corrected in combination with thermal response characteristic attenuation coefficients generated by service load characteristic analysis, and temperature distribution changes are predicted; generating a candidate control strategy set, screening an optimal strategy through composite scene simulation, and generating a control instruction; and comparing the actual temperature after the control instruction is executed with the model prediction temperature in real time, switching to a safety control mode when the deviation exceeds a threshold value, and extracting an incremental sample to update model parameters after the deviation is recovered, thereby realizing accurate temperature control.

The invention provides an airfield pavement detection method based on an earthquake background noise imaging technology, and relates to the technical field of airfield pavement detection. Comprising the following steps: acquiring seismic background noise data of an airport pavement to be measured; preprocessing the seismic background noise data to obtain preprocessed noise data; carrying out cross-correlation calculation on the preprocessed noise data and carrying out empirical Green function extraction; according to the empirical Green function, a surface wave group velocity frequency dispersion curve is extracted by adopting spatial autocorrelation and multiple filtering methods; inverting shear wave velocity distribution of each layer of the pavement based on the surface wave group velocity dispersion curve, and constructing a three-dimensional velocity model; determining a shear wave velocity distribution diagram according to the three-dimensional velocity model; and determining a detection result of the airfield pavement to be detected according to the transverse wave velocity distribution diagram. According to the invention, the problems of low detection precision and poor real-time performance of a common detection technology in the prior art are solved.

The invention belongs to the field of CT (Computed Tomography) tomography reconstruction technology and artificial intelligence, and discloses a finite angle CT reconstruction method based on combination of a three-dimensional conditional diffusion model and synchronous iteration. The CT is an imaging technology which utilizes X-rays to irradiate a target from different angles and acquire projection, and obtains an internal three-dimensional structure through reconstruction. Different from traditional CT depending on nearly full-angle scanning, the method only collects limited-angle projection, and achieves fault reconstruction under the limited-angle condition through a three-dimensional condition diffusion model of space domain-frequency domain two-way decoding and by means of structural generality priori of a workpiece. Meanwhile, projection and fault data consistency correction is carried out in combination with a synchronous iteration reconstruction technology, the advantages of an iteration method in the aspect of physical mechanism characterization are exerted, interpretable physical constraints are provided for a deep learning network, and therefore the reliability and precision of a reconstruction result are improved.

The invention relates to the technical field of image processing and traffic safety monitoring, in particular to a dynamic traffic marking inverse reflectivity intelligent monitoring system and a method thereof.According to the system, marking images are collected through a multispectral imaging technology, a precise space mapping relation is established through laser ranging, marking feature parameters are extracted from multi-band images, and the dynamic traffic marking inverse reflectivity intelligent monitoring system is obtained. A multi-dimensional feature matrix including reflection features, space geometry, time change and environmental influence is constructed, a nonlinear dynamic model of a traffic marking state is established based on the multi-dimensional feature matrix, a critical point and a bifurcation point in a degradation process are identified, and the system can calculate the change trend of the inverse reflectivity of the marking and generate multiple possible degradation paths. According to the method, the real-time dynamic monitoring of the inverse reflectivity of the traffic marking is realized, the measurement precision is improved, the prediction accuracy is enhanced, scientific decision support is provided for road maintenance, and the road traffic safety performance is remarkably improved.

The invention discloses an offshore wind power cable line fault early warning method and system, and relates to the technical field of cable line fault early warning, and the method comprises the following steps: laying a three-dimensional distributed optical fiber temperature array along an offshore wind power cable, and monitoring the cable thermal field space distribution in combination with an active acoustic dispersion imaging technology; acquiring initial evolution trajectory data of the edge high-temperature region; and on the basis of the initial evolution trajectory data, constructing a coupling relationship among the ocean current parameter, the sediment disturbance characteristic and the cable heat conduction parameter, forming an ocean current-sediment-heat conduction coupled digital twin model, and dynamically correcting a virtual scene of the cable burial depth in the model through a recursive filtering algorithm. Through combination of thermal field monitoring, digital twin modeling and intelligent intervention, accurate early warning and dynamic response of offshore wind power cable faults are realized, the operation and maintenance efficiency is improved, system self-optimization and cross-scene migration are supported, and the safety and intelligent level of cable operation are significantly enhanced.

The invention relates to the technical field of oral health monitoring, and discloses a real-time oral health monitoring system based on an intelligent imaging technology. The system comprises an image capture module, a feature analysis module, a health parameter calculation module, an anomaly detection module and a feedback control module. The image capture module captures dynamic image data in the oral cavity in real time and transmits the data to the feature analysis module; the feature analysis module preprocesses the image data, identifies key oral cavity structural features and generates a structured feature data set; the health parameter calculation module quantifies related health parameters based on the data set and integrates the related health parameters into an oral health parameter set; the anomaly detection module identifies the oral cavity area with the parameter value deviating from the normal range and outputs an anomaly detection result set; and the feedback control module generates optimization suggestions according to the optimization suggestions and drives the user interface display module to output a visual report. According to the system, real-time monitoring, automatic analysis and timely feedback of oral health are realized, and the monitoring efficiency and accuracy are 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 the technical field of smart park data monitoring, and discloses an artificial intelligence smart park multi-source data monitoring and analysis method. The method comprises the following steps: constructing a park operation state basic model; when the model is identified to be abnormal, data dynamic change characteristics are extracted by utilizing a spatio-temporal data fusion technology, and an abnormal event mode in a specific region is identified by combining deep belief network deep analysis; calculating data complexity by using permutation entropy, optimizing and analyzing an abnormal propagation path in combination with an A star algorithm, and evaluating a diffusion range and an influence path; recognizing a comprehensive risk area, analyzing the environment and equipment state characteristics of the area through a multispectral imaging technology, and evaluating the operation abnormity in combination with real-time multi-source data; and performing accurate intervention on the comprehensive risk area based on the abnormal condition. According to the method, effective integration and analysis of multi-source data of the smart park are realized, abnormity is accurately identified, risks are mastered, a scientific means is provided for park management, and stable and efficient operation of the park is guaranteed.

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 relates to an electric power cabinet alarm system based on image analysis, and relates to the technical field of images, and the system comprises the steps: carrying out the multi-angle image collection of an electric power cabinet, marking a time sequence, generating a time sequence marking image, and carrying out the preprocessing to obtain an enhanced image; carrying out region segmentation on the enhanced image to obtain a region image of the power cabinet assembly, carrying out texture analysis, and extracting surface state features; detecting whether surface abnormal deformation exists or not based on the features, if yes, judging an abnormal type, and obtaining abnormal type judgment data; and risk grade evaluation is performed according to the abnormal type, and a graded alarm instruction is generated, so that the technical problems that the traditional method mainly depends on manual visual inspection or regular maintenance, is easily influenced by the experience level and subjective judgment of inspection personnel, and is difficult to find potential hidden dangers in time are solved.

The invention discloses a multi-modal-based low-slow small target multi-dimensional detection method and system, and relates to the technical field of unmanned aerial vehicle detection. Omnibearing scanning is carried out on a target airspace through a radar, a suspicious target is determined through echo signal analysis, and initial position information of the suspicious target is acquired; adjusting the direction of the photoelectric turntable according to the initial position information, and capturing the suspicious target through a multi-band imaging technology to obtain a first target image set; preprocessing the first target image set to obtain a second target image set; and taking the second target image set as input of a pre-trained target detection model to obtain a target detection result. Through fusion of radar omnibearing scanning and multiband photoelectric imaging technologies, multi-mode and multi-dimensional detection of low-speed small targets is realized, and the problem that a single sensor is insufficient in recognition capability in a complex environment is effectively solved.

The invention relates to a high-signal-to-noise-ratio ultrasonic imaging method and device for internal defects of a curved surface component, a medium and a product, and belongs to the technical field of ultrasonic imaging. According to the method, a linear array ultrasonic probe is adopted to carry out single-time ultrasonic excitation on a curved surface component, a full-matrix echo signal is obtained, first arrival time extraction is carried out on the collected signal, an ultrasonic incident angle is estimated, and discrete coordinates of the surface of the component are obtained; the method comprises the following steps of: fitting a curved surface model, constructing an equivalent virtual array model, calculating transmitting and receiving delay time of each pixel point, realizing initial full-focusing beam forming of signals, estimating an expected signal variance and a noise variance based on maximum likelihood estimation, constructing an initial coherent beam forming machine and performing iterative updating, and finally forming a beam. And taking the coherent beam synthesizer obtained by iteration as a weighting factor, acting on an initial beam synthesis signal for weighting, and completing image reconstruction. The method has the advantages of being high in calculation efficiency, low in system overhead, high in imaging signal-to-noise ratio and the like, and accurate imaging can be achieved in a curved surface structure scene.

The invention provides an ultrasonic guided wave probability imaging method for structural damage in a temperature change environment, an improved U-Net neural network is constructed, input data of the improved U-Net neural network comprises an original guided wave signal and normalized temperature offset, and a model comprises a three-stage down-sampling module, a double-convolution layer and a three-stage up-sampling module. The attention mechanism module is connected behind the downsampling blocks of all levels and used for dynamically adjusting the feature weight, the representation capability of temperature features is enhanced, and the network can learn nonlinear mapping of temperature changes and signal changes. After model training data are obtained, filtering processing is carried out on original ultrasonic guided wave signals, normalized temperature deviation processing is carried out on temperature data, and the ultrasonic guided wave signals at the standard temperature are output through model prediction, so that the amplitude and phase interference of the temperature on ultrasonic guided waves is reduced, and the accuracy of ultrasonic guided wave detection is improved. And high-precision positioning of structural damage is realized in combination with a probability imaging technology. The method is small in data demand and few in execution steps, and the damage positioning efficiency is better than that of a traditional method.

The invention discloses a vehicle blind area dynamic enhancement and warning system for a low-light environment, and relates to the technical field of automobile active safety, and the system comprises a short wave infrared imaging module which is used for capturing a passive thermal feature image of a scene behind the side of a vehicle; the multi-source signal acquisition module is used for acquiring a vehicle bus signal representing a vehicle motion state and an operation intention of a driver in real time; the data processing and risk assessment module is connected with the short wave infrared imaging module and the multi-source signal acquisition module, and is used for performing fusion processing on the thermal feature image and the vehicle bus signal, identifying a blind area target and calculating a dynamic risk level of the blind area target; the grading warning and intervention module is connected with the data processing and risk assessment module; the short-wave infrared imaging technology which is sensitive to night light and high in anti-interference capacity is adopted, the imaging problem under extremely low illumination is fundamentally solved, and stable detection of vehicles and pedestrians in a long distance in a completely light-source-free environment is achieved.

The invention discloses a vector flow velocity measuring device and method based on a bidirectional scanning spectral domain optical coherence tomography technology. Comprising a super light-emitting diode light source, an optical fiber coupler, a polarization controller, an interferometer comprising a sample arm and a reference arm, and a collimating lens, the collimating lens, the grating, the focusing lens group and the linear array camera are sequentially connected through a light path; the linear array camera, the computer and the two-dimensional galvanometer are sequentially connected through a line; according to the method, the fixed bias vector velocity is introduced by utilizing the bidirectional scanning technology, the dynamic light scattering optical coherence tomography method and the Doppler optical coherence tomography method are combined, decoupling of each parameter of the vector flow velocity is efficiently and accurately realized, and a quantitative index is provided for vector flow velocity analysis. The vector flow velocity measuring device based on the bidirectional scanning spectral domain optical coherence tomography technology is simple in system setting and low in cost, the time resolution is improved by about 2.5 times, and the measuring time is shorter.

The invention relates to the technical field of industrial water treatment equipment, and discloses an intelligent flushing self-cleaning filter system for industrial water treatment. A dynamic suspended matter monitoring module of the system collects three-dimensional distribution data of deposits on the surface of a filtering medium in real time, and mixed deposition characteristics of suspended solids and oil stains are recognized through a multispectral imaging technology; the differential pressure trigger control module establishes a mapping relation between sediment thickness and fluid resistance according to the three-dimensional distribution data, and generates a cleaning trigger instruction when the real-time differential pressure data reaches a dynamic threshold interval; the spiral trajectory planning module analyzes the cleaning trigger instruction, and generates a composite motion trajectory containing a rotation angular velocity and an axial displacement rate in combination with the current position of a suction nozzle and a filter screen topological structure; the multi-mode flushing execution module synchronously adjusts torque output of a hydraulic motor and linear displacement of a piston cylinder based on the composite motion trail, so that a suction nozzle covers the whole effective filtering area of a filter screen along a spiral path.

The invention discloses a terahertz continuous wave wind power blade defect imaging method based on an unmanned aerial vehicle platform, relates to the technical field of blade defect imaging, and solves the problems that in the prior art, when a crawling robot serves as a contact type mobile platform to detect a wind power blade, the process is tedious and time-consuming, and safety risks exist. According to the method, an efficient and flexible non-contact detection mode is provided, the unmanned aerial vehicle is adopted as an aerial autonomous bearing platform, contact dependence on the surface of the blade is overcome, rapid deployment, long-distance maneuvering and flexible arrival of detection equipment are achieved, and the detection operation efficiency and safety of the high-altitude and in-service wind power blade are greatly improved; the invention further provides a split type terahertz transmission imaging method capable of being integrated with an unmanned aerial vehicle platform, visual and high-resolution imaging of bubbles, layering and other defects in the blade is achieved by means of the penetrating characteristic of terahertz waves to a composite material, and the defect that in the prior art, the internal defect detection capacity is insufficient is overcome.

The invention discloses a lining pipeline interface contact pressure distribution testing device and method, relates to the technical field of pipeline detection, and aims at solving the problem that in the prior art, interface contact pressure distribution is difficult to represent in a lossless and quantitative mode. The device comprises a testing machine moving platform, an overground control moving platform, a lining pipe wall testing claw, an ultrasonic signal transceiver and a three-dimensional imaging module, the testing claw carries four curved-surface ultrasonic probes, and signals are collected in a one-transmitting-multiple-receiving mode controlled by an FPGA (Field Programmable Gate Array); according to the method, a contact pressure characterization model is constructed through self-adaptive positioning and signal excitation and processing, and interface pressure distribution visualization is achieved in combination with a three-dimensional imaging technology. The method does not need to damage the pipeline structure, can accurately capture the spatial distribution characteristics of the contact pressure, is adaptive to the conventional detection of long-distance pipelines, provides a scientific basis for lining repair quality evaluation and pipeline operation safety, and is convenient to operate and high in applicability.

The invention belongs to the technical field of nondestructive testing, and relates to a method for imaging residual oil in a powder metallurgy component based on pulse thermal excitation, which comprises the following steps: applying preheating disturbance pulse to a target area to generate a pre-excitation thermal field; applying a main detection pulse to the same target area at an interval of a preset time lag, and generating a transient composite hot spot; obtaining the whole process from formation to cooling dissipation of the transient composite hot spot, and generating a dynamic thermal field image sequence; images in a specific time window are extracted, and a key stage hot spot evolution image set is generated; the physical state of residual oil in the target area is judged, and a residual oil state label is generated; performing mapping association on the residual oil state label generated at each detection position and the two-dimensional coordinate of the residual oil state label to generate a residual oil state visual image; according to the invention, the problem of missing detection or misjudgment caused by difficulty in effectively amplifying weak thermal physical property difference between oil liquids in different states due to single excitation mode in the existing thermal imaging technology is solved.

The invention provides a stratigraphic structure and ground pressure distribution detection method based on wave velocity tomography, and relates to the technical field of wave velocity tomography. The method comprises the following steps: firstly, arranging dense linear detector arrays in a monitoring area at an interval of 5m, and collecting the distribution condition of an aquifer, a lithology detection result and a drill core diagram of the monitoring area; recording seismic wave data, carrying out noise reduction processing on the seismic wave data, picking up arrival time, and associating the arrival time with a waveform event; performing path tracking on an observation result by adopting a wave velocity inversion algorithm, generating a wave velocity tomography image and substituting the wave velocity tomography image into the wave velocity-ground pressure evaluation model; and finally generating a ground pressure distribution diagram.

The invention provides a self-adaptive multi-frame accumulation detection imaging method used in a dynamic background irradiation environment, belongs to the technical field of space target imaging, and aims to solve the problems of low imaging quality, image information loss and a dynamic background of trailing in the dynamic background irradiation environment. Setting initial integral time to shoot the to-be-detected area, and setting panoramic integral time according to a shooting result to shoot again; 2, dividing the re-shot area into a plurality of rectangular areas; step 3, setting different sampling time for each divided area for sampling; step 4, performing multi-frame accumulation on the sampled image until a new faint target is identified; and 5, for a new faint target, repeating the steps 2-4 to perform sampling and multi-frame accumulation until the sampling time reaches the specified total integral time, and outputting a detection image.

The invention belongs to the field of train structure health monitoring, and discloses a bogie tube plate structure welding seam detection method and device, and the method comprises the steps: obtaining a Lamb wave signal of a to-be-detected tube plate structure welding seam region, and carrying out the preprocessing, and obtaining a preprocessed Lamb wave signal; on the basis of the preprocessed Lamb wave signal, the position of the weld crack defect of the to-be-detected tube plate structure is predicted through the RAPID tomography technology, and a weld crack defect positioning detection result is obtained; inputting the preprocessed Lamb wave signal sequence into a bogie tube plate structure welding seam detection model to obtain a welding seam crack defect quantitative detection result; outputting a welding seam crack defect positioning detection result and a quantitative detection result as welding seam detection results; according to the invention, the positioning and quantitative detection result of the welding seam damage of the bogie tube plate structure can be quickly and accurately obtained, the high-precision detection requirement of the welding seam of the bogie tube plate structure is met, and the safe operation of a rail transit vehicle is ensured.

The invention discloses a multispectral imaging technology-based maintenance method, device and equipment, and the method is applied to the maintenance equipment, and comprises the steps: determining a light supplement strategy according to the environment data of a target region, and carrying out the light supplement of the target region based on the light supplement strategy; acquiring a first spectral image, a second spectral image, a third spectral image and a fourth spectral image for the target area; performing fusion processing on the first spectral image, the second spectral image, the third spectral image and the fourth spectral image to determine a spectral feature map; performing feature analysis on the spectral feature map to determine fault information in the target area; according to the method, the multi-spectral imaging technology is used for detecting the hidden defects on the surface of the equipment facility, the fault can be accurately recognized, the position and the specific type of the fault can be determined, and the recognition accuracy and efficiency are remarkably improved.

The invention relates to the technical field of SAR (Synthetic Aperture Radar) remote sensing imaging, in particular to a scattering heuristic SAR tomography method for structured three-dimensional reconstruction, which comprises the following steps of: S1, acquiring and preprocessing multi-baseline SAR data; s2, inverting scattering parameters based on an attribute scattering center model and orthogonal matching pursuit; s3, segmenting a building area according to coherence and morphological indication; s4, combining direction perception expansion with region updating, determining a continuous scattering region, and giving reference height prior; and S5, constructing a regional adaptive dictionary, sparsely solving the elevation and geocoding to generate a structured point cloud. According to the method, physical prior and regional statistics are cooperated, geometric continuity and anti-noise robustness are improved, pseudo peaks and calculation redundancy are reduced, and upgrading from sparse point cloud to a structured model is 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 relates to the technical field of panoramic images, and discloses a self-correcting panoramic image splicing method for a vehicle around view system, which comprises the following steps: acquiring dynamic data of a vehicle to obtain an environment characteristic dynamic state, establishing a space-time incidence matrix according to the environment characteristic dynamic state, adjusting a fuzzy kernel estimation parameter, and performing accurate estimation and multi-frame super-resolution reconstruction of motion blur; fusing dynamic data, defining a vehicle attitude feature matrix, dynamically correcting vehicle attitude and camera external parameters, unifying cross-camera colors, performing ground constraint, constructing a multi-objective optimization framework, and balancing splicing quality and real-time performance; through monocular depth estimation and geometric constraint, the perspective difference of a close-range object is optimized, and through an edge retention loss function, the depth precision at the boundary of the object is enhanced; monocular depth estimation is optimized through binocular parallax constraint; constructing a multi-scale feature fusion network, and recovering image details; a factor graph model is constructed to integrate historical data, and accumulative errors of long-time-sequence splicing are eliminated.