337 results about "High resolution imaging" patented technology

The invention relates to the technical field of shock absorber defect detection, and discloses an electric vehicle shock absorber defect detection method and system based on machine vision, and the method comprises the steps: collecting an initial image set under the irradiation of a multi-angle light source through high-resolution imaging collection equipment; performing denoising and filtering processing according to the initial image set to obtain clear image data; performing defect classification and spatial distribution analysis according to the clear image data to obtain surface feature vectors containing defect types and defect spatial distribution; carrying out vibration amplitude acquisition and phase angle measurement operation according to the surface feature vector, and carrying out spectral analysis to construct a performance parameter vector; performing data fusion according to the performance parameter vector and the surface feature vector to obtain a fusion feature; inputting the fusion features into a pre-constructed association prediction model to obtain defect prediction data; and performing defect influence degree analysis according to the defect prediction data to obtain a defect evaluation result. The method provides a basis for quality control and performance optimization of the shock absorber.

The invention discloses a method for predicting plant photosynthetic ability by using plant leaf traits, and relates to the technical field of agricultural science, and the method comprises the following steps: obtaining image data of a target plant leaf in a natural state through a high-resolution imaging device, and synchronously recording environment parameter information during collection; extracting multi-dimensional feature parameters of the leaf based on the image data, wherein the multi-dimensional feature parameters comprise a color index, a texture feature, an area, a thickness estimation value and an edge contour form; and carrying out stress feature identification on the character data by utilizing an association rule of the environmental parameters and the leaf characters. According to the invention, by introducing a stress identification and data correction mechanism, the accuracy of photosynthetic capability prediction is improved; the method has good environmental adaptability and crop universality, and is suitable for various agricultural scenes; and a prediction feedback closed-loop system is constructed, continuous optimization and intelligent adjustment of the model are realized, and the stability, reliability and practical application value of the system are remarkably enhanced.

The invention relates to the technical field of crop disease and insect pest image recognition, and particularly discloses a crop disease and insect pest image recognition method based on a large model, and the method comprises the steps: obtaining a multi-angle leaf image through high-resolution imaging equipment under a controllable illumination condition, and obtaining a target image in a unified format; extracting scab texture complexity features in combination with a local binary pattern and a gray-level co-occurrence matrix algorithm, and performing multi-channel statistical analysis on RGB and HSV color spaces to generate color heterogeneity feature vectors; further fusing the two types of features into a composite disease feature vector, inputting the composite disease feature vector into a probability model constructed based on a support vector machine and a Monte Carlo Dropout mechanism, and outputting probability distribution and confidence score of disease and pest categories; and dynamically adjusting a model training strategy according to a confidence level, triggering a feedback mechanism for a low-confidence sample, generating a synthetic image by using a conditional generative adversarial network, and optimizing model parameters in combination with incremental learning to realize stable identification modeling of rare or complex disease types.

The invention relates to the technical field of PTFE (polytetrafluoroethylene) membrane micropore defect detection, in particular to PTFE membrane micropore defect detection equipment based on machine vision, which comprises symmetrically arranged mounting side plates and sensor assemblies arranged on one sides of the mounting side plates at intervals, and an overturning assembly used for realizing driving is arranged between the two groups of mounting side plates. Adjusting assemblies are arranged on the outer side of the overturning assembly in an array mode, and detection modules used for detecting PTFE membrane micropore defects are arranged on the outer sides of the adjusting assemblies. And the adjusting assembly comprises clamping frames arrayed on the outer side of the overturning assembly, bent plates are symmetrically arranged on the sides, close to the overturning assembly, of the clamping frames, and quick connecting rods are installed on the inner walls of the bent plates at intervals up and down. The device has the beneficial effects that high-resolution imaging is carried out by adopting the intelligent sensor, the white light interferometer and the high-speed line scanning camera, micro-current detection is realized in combination with weak current released by the discharge piece, micropore defects can be accurately identified, and misjudgment or missing detection caused by a single detection mode is avoided.

The invention discloses a large-view-field high-resolution imaging system based on polarization regulation and control, the large-view-field high-resolution imaging system comprises a metasurface system array and a camera along the direction of a light path, and the metasurface system array comprises a plurality of pairs of metasurface systems which are distributed in an array; each pair of metasurface systems comprises a first metasurface system and a second metasurface system; the first metasurface system is used for performing high-resolution imaging and large-view-field imaging on a first polarization light beam and a second polarization light beam of an incident light beam respectively; the second metasurface system is used for performing large-view-field imaging and high-resolution imaging on the first polarization-state light beam and the second polarization-state light beam respectively; the camera is used for acquiring and fusing images formed by different metasurface systems so as to capture a high-resolution central region of interest and surrounding environment information of a wide field of view. The system can efficiently realize higher imaging quality.

The invention provides an imaging optical system for industrial visual inspection, which is characterized in that a first lens is mainly composed of nine lenses and has focal power, the object side surface of the first lens is a convex surface, and the image side surface of the first lens is a concave surface; the second lens has focal power; the third lens has focal power; the fourth lens has focal power; the fifth lens has positive focal power; the sixth lens has focal power, and the object side surface of the sixth lens is a convex surface; the seventh lens has focal power, and the object side surface of the seventh lens is a concave surface; the eighth lens has focal power; and the ninth lens has positive focal power, the image side surface of the ninth lens is a convex surface, and through the combination design of nine lenses and the reasonable configuration of the refractive power and the surface type of each lens, high-resolution imaging under a compact structure and a large aperture is realized, the aberration correction capability is particularly enhanced, and a relatively high MTF value is ensured in the whole zoom range. Accuracy and reliability of industrial detection are remarkably improved, and the system has the advantages of zooming, low distortion and low cost.

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 provides a bistatic ISAR imaging arc section selection method for an air moving target, and relates to the technical field of bistatic radar imaging, and the method comprises the steps: obtaining the position information of a transmitting station, a receiving station and the air moving target, and converting the position information of the transmitting station, the receiving station and the air moving target into the coordinates of an earth-centered earth-fixed coordinate system; establishing a translation simulation model, a rotation simulation model and an echo model of the air moving target; the method comprises the following steps: determining a first constraint condition and a second constraint condition of a double-base-angle change rate of an air moving target according to a translation simulation model, a rotation simulation model and an echo model of the air moving target; selecting a target imaging arc section according to the earth-centered earth-fixed coordinate system coordinates of the transmitting station, the receiving station and the air moving target; therefore, the high-resolution imaging of the air moving target can be quickly and accurately obtained according to the target imaging arc segment, and the processing efficiency and the image quality can be effectively improved.

The invention discloses a scattered wave seismic acquisition and imaging method based on a full random observation system, which belongs to the technical field of seismic exploration and comprises a quasi-Monte Carlo random observation array design step, a scattered wave intelligent identification and enhancement step, a sparse wave field reconstruction step and a multi-target optimization imaging step. The limited equidistant property is ensured through the random observation array design based on a low-difference sequence, intelligent separation and enhancement of scattered waves are realized by using a physical constraint neural network, a sparse reconstruction problem is solved by using an alternating direction multiplier algorithm, a multi-target optimization imaging framework is constructed to realize high-resolution imaging, and high-resolution imaging is realized. The four core steps form a closed-loop cooperative system, sparse reconstruction quality evaluation feedback optimizes observation array design, imaging quality measurement feedback adjusts scattered wave identification parameters, and cooperative interaction is achieved.

The invention relates to a method and system for gradient drying of butyronitrile gloves after impregnation in the field of intelligent manufacturing, and the method comprises the steps: obtaining surface and internal moisture content data of an adhesive film through a sensor array, generating a real-time moisture distribution matrix, and determining a moisture gradient change trend; if the moisture gradient change trend exceeds a preset threshold value, calculating a local moisture evaporation rate according to the distribution matrix to obtain a dynamic temperature and humidity regulation and control parameter; obtaining an adhesive film surface image through a high-resolution imaging system, detecting micro-crack and white point defect areas, and determining defect distribution characteristics; adjusting power distribution of the composite heating system according to the lowest energy consumption operation parameters, and generating an efficient drying operation mode; updating data of the dynamic regulation and control parameters and the defect distribution characteristics are obtained through a feedback control algorithm, and final drying process parameters are determined; and generating a production control instruction from the final drying process parameters, and outputting the production control instruction to a drying equipment controller to finish uniform diffusion of moisture of the adhesive film and elimination of surface defects.

The invention discloses an organic rare earth luminescent complex scintillator as well as preparation and application thereof. The molecular structural formula of the complex is L13ML2, M is Tb, Eu or Er metal ions, and the valence state is + 3; l1 is a tetraphenyl diphosphine imide anion ligand (tpip) or a substituted tpip ligand; l2 is unsubstituted or substituted 2, 2 '-bipyridyl, 1, 10-phenanthroline and a non-ionic chelating group containing a polyheterocycle / fused heterocycle. The organic rare earth luminescent complex scintillator can effectively convert high-energy X-rays into narrow-band visible light and near-infrared light, and is suitable for being applied to the fields of high-energy ray detection and high-resolution imaging.

The invention relates to the technical field of medical imaging, and discloses a peripheral lung cancer pleural invasion evaluation system based on ultrasonic targeted radiography, comprising: a double-frequency ultrasonic probe configured to independently work at different frequencies to realize depth imaging and high-resolution imaging of lung tissues and suitable for different types of lung lesions; the signal processing module can receive, analyze and process echo signals transmitted by the ultrasonic probe, generate images in real time and extract feature information; and the dynamic motion compensation module is used for automatically adjusting the emission time sequence and parameter setting of the ultrasonic signal based on the real-time breathing motion state of the patient. By adopting the design of the double-frequency ultrasonic probe and the core-shell structure, high-resolution imaging and depth detection of lung tissues are realized. By dynamically adjusting the thickness of the probe shell to be matched with the frequency, the system can adapt to complex tissue environments under different physiological conditions, and image definition and stability are ensured.

The invention relates to the technical field of radar target characteristic analysis, in particular to a radar near-field target polarization characteristic analysis method based on a vector Green function. Firstly, a target three-dimensional scattering point model is constructed, a coordinate-based scattering coefficient is defined, a full-polarization observation mode is designed, a near-field measurement matrix is calculated based on a vector Green function, and an observation model is constructed. Secondly, designing a fusion far-field constraint orthogonal matching pursuit (OMP) algorithm, realizing high-precision inversion of scattering coefficients, calculating a target far-field single-station RCS (HH / HV / VV mode) through dynamic polarization-based projection and phase compensation, and establishing a near-far field scattering characteristic mapping model; according to the method, near-field three-dimensional imaging and far-field polarization characteristic analysis are fused, the problem of insufficient cross polarization component recovery in a traditional method is solved, and the method has the advantages of high-resolution imaging, multi-physical field collaborative optimization, adaptive noise suppression and the like, and can be applied to the fields of stealth target recognition, radar cross section testing, unmanned aerial vehicle navigation and the like.

The invention relates to a color laser printer printed product identification system and method, and the system comprises a scanning module which is used for scanning a printed product printed by a printer to form a first image, and the printed product is provided with a special mark; the image processing module is used for preprocessing the first image to obtain a second image; the transcoding module is connected to the image processing module or the scanning module, and is used for identifying a special mark in the first image or the second image to obtain a single mark matrix representing printing information; and the decoding module is used for interpreting the single mark matrix to obtain printing information corresponding to the printed product. The scanning module captures special marks on the surface of a printed product through a high-resolution imaging technology, and complete extraction of tiny marks is ensured. The image processing module performs preprocessing such as noise reduction, contrast enhancement and geometric correction on an original image, so that the definition and identifiability of a mark are remarkably improved, and interference factors such as paper wrinkles and stains are effectively handled.

The application discloses a kind of nnUNet zebra fish juvenile whole brain vascular system segmentation methods based on autonomous data set training, it is related to high-resolution imaging technology, image processing and medical image segmentation field, the method makes full use of zebra fish live transparency and fluorescent label advantage, obtains high-resolution whole brain three-dimensional vascular image data, and constructs high-quality segmentation truth value database by semi-automatic segmentation and artificial correction, training is carried out using nnU-Net deep learning model, realize the three-dimensional automatic segmentation of zebra fish brain vascular system signal.The application method significantly improves the degree of automation and precision of image segmentation, effectively solves the problems of low efficiency, high artificial dependence and poor repeatability of traditional brain vascular segmentation.The method is suitable for large-scale high-throughput data processing, can provide efficient, standardized image processing scheme for zebra fish brain vascular development mechanism and brain vascular disease model research, and has wide application prospect.

The invention relates to an aliasing image target recognition and positioning algorithm based on sub-view-field shape modulation coding, relates to the technical field of aliasing image target recognition and view field judgment and positioning, and solves the technical problem that the scale and view field of a detector cannot be considered when large-view-field and high-resolution imaging is realized under the condition of a limited detector in the prior art. The algorithm comprises the following steps: identifying a foreground target; tracking a target rotation state; and judging the relevance between the target motion trail and the light spot shape change trend and inversely solving the actual spatial position. According to the aliasing image target recognition and positioning algorithm based on sub-view-field shape modulation coding, space-based early warning and large-width detection are achieved through the advantages of low power consumption and low cost of a small target surface detector, and the bottleneck that large-view-field imaging of an infrared optical system is limited by the manufacturing process of an infrared detector is broken through. According to the invention, through continuous frame light spot morphological feature extraction and trajectory-shape double-domain correlation modeling, unique determination and high-precision positioning of a view field of an object space where a target is located are realized.

The invention relates to the technical field of machining, and discloses a single-crystal high-temperature alloy grinding machining plastic deformation layer thickness identification method and system, and the method comprises the steps: collecting a port section image through the high-resolution imaging capability of a scanning electron microscope, and carrying out the preprocessing of the collected image, utilizing a Canny edge detection algorithm to accurately identify edge information in the image; performing crystal edge frame identification by using a Hough LinesP rectangular detection algorithm to calculate the crystal direction angle of each crystal in the image to distinguish the crystals in an undeformed area and a deformed area and the maximum depth value of the crystals in the corresponding area, and combining a preset crystal angle identification threshold value in a plastic deformation area after the single crystal high-temperature alloy material is ground and processed to identify the crystal edge frame of the single crystal high-temperature alloy material. And obtaining the thickness value of the plastic deformation area after the single-crystal high-temperature alloy material is ground. According to the method, the thickness value of the plastic deformation area of the ground single-crystal high-temperature alloy material can be accurately analyzed, and powerful technical support is provided for control and optimization of material machining quality.

The invention belongs to the technical field of radar imaging, and particularly relates to a radar sparse three-dimensional imaging and resource scheduling method based on ring array nesting, which comprises the following steps of: establishing a sparse imaging model under an OFDM (Orthogonal Frequency Division Multiplexing) framework; providing a sparse two-dimensional imaging method; proposing a pitch angle reconstruction method based on phase interference; calculating imaging parameters required by resource allocation in the multi-target imaging scene; and establishing an adaptive resource scheduling optimization model and providing a solving method. According to the radar sparse three-dimensional imaging method based on circular ring array nesting, three-dimensional high-resolution imaging is achieved under the condition that a small number of radar resources are consumed. And a self-adaptive resource scheduling algorithm is provided, so that a multi-target imaging self-adaptive resource scheduling model is established by taking time saving, improvement of array element and frequency band utilization rates and balance of radiation intensity of a mode selected by each target as optimization targets, and reasonable allocation of limited radar resources is realized.

The invention discloses an image processing system and method for salt mist particle size detection, and relates to the technical field of salt mist tests, the system captures salt mist particle images and acquires multi-dimensional information through a multi-spectral imaging technology by using light sources with different wavelengths and high-resolution imaging equipment, and establishes an image information set in combination with environment temperature and humidity data. Through image preprocessing and multispectral image fusion, salt mist particle features are extracted, and a comprehensive data set is constructed. And calculating a salt mist particle size distribution coefficient and a particle concentration coefficient according to the comprehensive data set to ensure that experimental conditions meet preset requirements and avoid experimental errors. Further analyzing the motion change and the form change of the particles, and calculating a dynamic change coefficient to ensure the stable behavior of the particles. And finally, training and screening the data by using a convolutional neural network, automatically identifying the salt mist particles conforming to the standard, and optimizing the accuracy and efficiency of the salt mist experiment.

The invention relates to the technical field of tea garden insect pests, and discloses a tea garden insect pest early warning system based on machine learning, which comprises a multi-mode sensing device, an edge intelligent terminal and a cloud decision platform. According to the tea garden insect pest early warning system based on machine learning, a narrow-band spectrum is dynamically selected through an adjustable spectrum imaging unit, image and sound wave features are synchronously acquired in combination with a vibration wave acquisition module, a heterogeneous calculation module of an edge intelligent terminal processes multi-source data in parallel, and a dynamic feature library management module updates a feature template in real time; the incremental learning mechanism triggers high-resolution imaging when unknown pests are detected, the meta-learning framework supports rapid migration training of a new model, the framework breaks through the limitation of a traditional single spectrum and a fixed model, and through multi-dimensional data fusion and adaptive learning, the pest recognition efficiency and adaptability are remarkably improved. The problems of low manual inspection efficiency and poor adaptability of traditional equipment are solved.

The invention discloses a tissue slice high-resolution mass spectrum imaging method and system based on multi-modal data fusion, and belongs to the technical field of mass spectrum imaging and biomedical detection. The method comprises the steps of multi-modal data acquisition, data preprocessing, feature extraction, multi-modal data fusion and high-resolution imaging reconstruction. High-resolution mass spectrum data and high-stability auxiliary modal data are synchronously collected, after preprocessing and feature extraction, feature weights are dynamically distributed and fused by using a fusion model based on an attention mechanism, and then a high-resolution and high-sensitivity imaging result is output through an imaging reconstruction model of a U-Net improved architecture. On the premise that the spatial resolution is not reduced and the sample is not damaged, the mass spectrum signal intensity is effectively enhanced, the low-abundance biomarker recognition capability is improved, the imaging efficiency is high, the adaptability is high, and the method is suitable for biomedical research and clinical diagnosis scenes.

The invention discloses a rockburst risk identification method based on a dynamic interference compensation algorithm and quasi-three-dimensional high-resolution imaging. The rockburst risk identification method comprises the following steps: separating construction noise and a rock mass fracture signal in real time by adopting a variational mode decomposition-particle swarm optimization algorithm; a high-frequency tunnel electromagnetic signal and a rock mass fracture signal are obtained, and an inversion imaging image is obtained through quasi-three-dimensional inversion of multi-band data fusion; and according to the inversion imaging image, the fracture density and the extension direction are extracted, and the trained LSTM model is used to carry out rockburst risk identification. Through the scheme, the method has the advantages of simple logic, accuracy, reliability and the like, and has very high practical value and popularization value in the technical field of tunnel construction safety monitoring.

The embodiment of the invention provides a method and device for detecting a mine water guide channel, electronic equipment, a storage medium and a program product. The method comprises the following steps: in response to acquired seismic shot gather data associated with a mine target area, separating a time domain diffracted wave signal from the seismic shot gather data; performing dip angle domain conversion on the time domain diffracted wave signal by using a preset conversion rule to obtain a dip angle domain diffracted wave field; wherein the dip angle domain diffracted wave travel time equation is involved in the dip angle domain diffracted wave field; based on the travel time equation, utilizing Taylor approximation to construct a diffracted wave target function; on the basis of the target function, diffracted wave event inversion operation is executed, and an optimal superposition path is obtained; and based on the dip angle domain diffracted wave field and the optimal superposition path, performing superposition operation, and generating a water guide channel imaging result of the mine target area. By utilizing the method disclosed by the embodiment of the invention, the speed sensitivity limitation of a traditional method can be broken through, and high-resolution imaging of a small-scale water guide structure is realized.

The invention relates to the technical field of infrared optics, in particular to a large-area-array long-rear-working-distance infrared optical system which comprises a head cover, a first lens, a second lens, a third lens, a fourth lens and a detector, the head cover serves as an optical window, the specific surface curvature radius and thickness design of the head cover optimizes the light incidence and preliminary convergence effect, and the light intensity is improved. The structure of the four lenses is reasonably distributed according to positive and negative focal powers to form a stable optical structure, and the diffraction surface design of the first lens can effectively correct chromatic aberration and improve the imaging quality. Meanwhile, the second emergent surface, the third incident surface, the fourth emergent surface and the fifth incident surface all adopt aspheric surface design, aberration is further reduced, and high-resolution imaging of the large-area-array detector is guaranteed. In addition, through reasonable spacing arrangement between the elements, the long rear working distance of the system is realized, and the requirements of adapting to detectors of different models are met. In conclusion, the system successfully realizes an imaging target with a large area array and a long rear working distance.

The application discloses a kind of multiscale unmanned aerial vehicle image monitoring crop growth methods and systems, belong to agricultural remote sensing and intelligent monitoring technical field.The method of the present application comprises: controlling unmanned aerial vehicle to collect target area low-resolution remote sensing image at first height;Based on first identification model, crop land boundary is extracted and divided into multiple grids;The health index of each grid is calculated, and the center point of the abnormal grid is located based on the statistical threshold value;Control unmanned aerial vehicle to descend to second height and fly to the top of abnormal grid to carry out leaf scale high-resolution imaging;High-resolution image is input into second identification model, and output fine diagnosis results such as water shortage, lack of fertilizer, disease and pest.The present application realizes the organic combination of large-scale rapid screening and local accurate diagnosis through two-level scale cooperative monitoring, greatly improves the efficiency and accuracy of crop growth monitoring, and is suitable for field crop automation, intelligent growth management.

The application belongs to the technical field of tunnel electromagnetic exploration, and particularly relates to a method and system for rapidly positioning anomalies by using transient electromagnetic exploration in a well, which comprises the following steps: constructing a coaxial orthogonal detection array composed of four transmitting coils and one receiving coil and lowering the array into a borehole; adjusting the position of the transmitting coils so that the receiving coil is in a primary field zero magnetic flux state; preliminarily judging the anomaly body azimuth quadrant and depth range through grouped transmission and rotating observation, and combining 90-degree phase difference excitation to improve the positioning accuracy to a 45-degree cone angle range; obtaining a pure secondary field response through equivalent anti-phase transmission of side coils to realize high-resolution imaging of the shallow part; and enhancing the magnetic moment through in-phase transmission of all coils to realize deep tracking detection. The application realizes rapid and accurate positioning of the full-space anomaly body in the borehole, and is suitable for the fields of tunnel advanced prediction, mine goaf detection and the like.

The application discloses a kind of real synthetic aperture imaging methods for multichannel radar forward-looking imaging, first, the echo data of the region to be imaged is obtained, second, the data obtained is compressed in range direction pulse, then the data after pulse compression is respectively processed in synthetic aperture and real aperture, for synthetic aperture dimension, using BP algorithm is focused in azimuth direction, and the results of each channel are incoherently accumulated, to obtain left-right ambiguous image, for real aperture dimension, the imaging scene is divided into grid, constructs guide matrix, reconstructs target scattering coefficient to realize super-resolution imaging, finally, the results of two dimensions are multiplied, to obtain non-ambiguous and super-resolution result.The method of the application fully utilizes the advantages of high resolution obtained by platform motion synthetic aperture in squint region and high resolution obtained by azimuth real aperture super-resolution in forward-looking region, overcomes the problem of single-base radar forward-looking imaging ambiguity and low resolution, and realizes multichannel radar non-ambiguous forward-looking high-resolution imaging.

The utility model relates to the technical field of optical devices, in particular to a shutter assembly and a slit type large-format scanning camera, the shutter assembly comprises a film box, a slit scanning mechanism and a linear driving mechanism, and a photosensitive component is arranged in the film box; the gap scanning mechanism comprises a bearing part and a rigid insertion sheet; the bearing part is in guide fit with the film box; the rigid insertion sheet is inserted into the film box, two ends of the rigid insertion sheet are fixed on the bearing component, and a gap is formed in the rigid insertion sheet; the linear driving mechanism comprises a supporting part, a driving part and a transmission part, and the driving part is arranged on the supporting part; the transmission part is arranged on the supporting part and is in transmission fit with the driving part, and the transmission part is connected with the bearing part. According to the utility model, the driving part and the transmission part in the linear driving mechanism are matched to realize the rapid and slow linear motion of the bearing part, so as to drive the rigid insertion sheet to stably scan the photosensitive part and ensure that the light is irradiated on the photosensitive part in an accurate slit form, and the high-quality and high-resolution imaging effect can be obtained.

The invention discloses a bending endoscopic imaging method and system based on a few-mode optical fiber, and belongs to the technical field of optical fiber imaging. According to the method, an MNIST handwritten digital image is loaded through a digital micromirror device (DMD), a speckle pattern is generated through few-mode optical fiber transmission, image restoration in a bending state is achieved through a deep learning model, and reconstruction effects under different bending degrees are quantized through a Dice coefficient. The system comprises a light beam collimation module, a pattern loading module, an image transmission module and an image acquisition-processing module. The image transmission module adopts a step refractive index few-mode optical fiber, and mode coupling caused by bending is inhibited by increasing the effective refractive index difference between modes; and the image acquisition-processing module is combined with a shallow U-shaped convolutional neural network to reconstruct a speckle pattern acquired by the CCD camera. While miniaturization of the probe is ensured, the imaging quality in a bending environment is remarkably improved, and the probe is suitable for miniaturized high-resolution imaging scenes such as medical endoscopes and the like.

The invention discloses a high-resolution imaging method and system for the surface of the inner wall of a cavity hole of a complex workpiece. The imaging system extends into the workpiece cavity hole at a constant speed along the centroid axis direction of the workpiece cavity hole, and acquires images at a preset frame rate to obtain a plurality of frames of cavity hole inner wall images; extracting the plurality of frames of cavity hole inner wall images to obtain an annular scanning area of each frame of cavity hole inner wall image; expanding the annular scanning area in each frame of cavity hole inner wall image through coordinate mapping to obtain a rectangular expanded view of the annular scanning area in each frame of cavity hole inner wall image; and splicing the rectangular expanded images of the annular scanning areas in the cavity hole inner wall images according to an acquisition sequence to obtain a panoramic expanded image of the cavity hole inner wall of the workpiece. According to the invention, a high-resolution panoramic unfolded image of the inner wall surface can be obtained, and the method is suitable for visual detection of the inner wall surfaces of workpiece cavity holes with various complex section shapes.