878 results about "Linear array" patented technology

The invention discloses an intelligent control and detection system for precision coating uniformity, and relates to the technical field of non-contact thickness measurement, the system comprises a spectrum confocal chromatography detection module, a non-contact multi-wavelength full-breadth detection module, an optical flow field dynamic measurement module and the like, a data processing unit is combined with a model to generate an evaluation model, and the evaluation model is used for evaluating the coating uniformity. The system has the advantages that multi-layer coating structure data are obtained in a full-breadth mode through a linear array sensor of the spectrum confocal chromatography detection module, a slurry full-breadth flow velocity vector field is analyzed in combination with the optical flow field dynamic measurement module, and the flow velocity measurement accuracy is improved. And the data processing unit fuses the multi-modal data to generate an evaluation model, and the regulation and control strategy generation module outputs an instruction containing edge air pressure regulation, so that accurate control over coating uniformity is achieved, damage to a base material is avoided, material waste and manual debugging time consumption are reduced, and the production efficiency and the product yield are remarkably improved.

The invention relates to the technical field of grain detection, and discloses a grain broken rice rate online detection method and system based on image analysis. A grain broken rice rate online detection method based on image analysis comprises the steps of intelligent illumination control and high-speed synchronous imaging, self-adaptive image enhancement based on reinforcement learning, multi-stage parallel particle detection and segmentation, multi-modal feature deep fusion, robust classification based on ensemble learning, intelligent quality control and process optimization. And continuously learning and updating knowledge. A clear image is obtained through a multi-angle linear array camera and a stroboscopic light source, a reinforcement learning agent is adopted to carry out adaptive image enhancement, a deep learning network is used to carry out particle detection segmentation, multi-dimensional features are extracted, accurate identification is realized through an integrated classifier, and a digital twin model is established to carry out process parameter optimization. According to the invention, accurate online detection of the broken rice rate of grains can be realized in a high-speed flowing state, and the detection efficiency and accuracy are improved.

The invention relates to the technical field of satellite internet, and discloses a low-orbit satellite phased array multi-beam interference modeling and suppression method and system, and the method comprises the steps: selecting a Kaiser window as a core filtering method, and achieving the optimization of beam characteristics through the dynamic adjustment of a shape parameter beta; generating an initial beam directional diagram based on a digital phase matching method, and multiplying the Kaiser window function coefficient by the excitation weight of the 64-array-element linear array element by element to realize spatial domain weighted filtering; the method comprises the following steps: constructing a training data set containing multi-scene interference characteristics, calculating a corresponding covariance matrix and an accurate inverse matrix thereof to form a sample pair, designing a deep neural network architecture, inputting a flattened covariance matrix vector, and learning a complex nonlinear mapping relation from the covariance matrix to the inverse matrix through a multi-layer full-connection structure; a mean square error is used as a loss function to constrain network output precision, and a multi-beam interference system model is constructed; according to the invention, stable and efficient communication of the low-orbit satellite system in a complex electromagnetic environment and under rapid channel change is ensured.

The invention relates to the field of artificial intelligence and computer vision, discloses an AI vision-driven polyester fabric pilling detection method and system, and aims at solving the problems that traditional manual visual inspection is low in efficiency and high in subjectivity, and existing machine vision is insufficient in sensitivity to low-contrast tiny defects. The method comprises the steps that fabric images are collected through a high-resolution linear array camera and a multi-channel self-adaptive LED light source, after non-uniform correction and self-adaptive filtering, the fabric images are input into a three-level convolutional network to extract multi-scale texture features, and then unified representation is generated through cross-scale attention fusion; according to the technical scheme, high-precision recognition of pilling with the diameter smaller than 0.5 mm is achieved, the detection rate is increased by 42%, the false alarm rate is lower than 3%, full inspection of a 60-meter / minute production line is supported, structured quality data are output, and the intelligent and quantitative level of online quality inspection in the textile industry is remarkably improved.

The invention relates to the technical field of size detection, in particular to a component size detection system based on machine vision, which comprises an interference area identification module, an edge repair module, a structure clustering module, a distortion analysis module and an attitude restoration module. According to the method, the gray level change trend of each pixel and the adjacent points in the image is obtained, the high-reflection suspected interference area is identified, the edge response cancellation processing is executed, and linear interpolation compensation is carried out by combining the effective edge response values of the adjacent areas; after a continuous contour trend is constructed, clustering and sub-grouping division are carried out according to an included angle difference trend between contour geometric vectors, a three-axis direction response linear array variation amplitude is extracted to construct a distortion curvature mapping table, existence of non-equal-interval deformation is judged, reverse angle compensation is applied, and a three-axis intersection point convergence process is completed through a nonlinear optimization algorithm; and according to the three-dimensional coordinates, calculating a geometric dimension and outputting a detection result, thereby realizing self-adaptive detection of different postures and surface interference component dimensions.

The invention discloses a multi-scale linear array camera splicing method and system based on point cloud. The method comprises the following steps: completing acquisition and preprocessing of point cloud data and image data of a target area; determining an overlapping region range between adjacent images; extracting spatial structure characteristics in the point cloud data, and performing multi-scale hierarchical decomposition on the point cloud through a multi-scale segmentation method; meanwhile, multi-scale image feature extraction is carried out on the images of the linear array camera; solving gradients in X and Y directions by adopting an optical flow method aiming at any pixel in the overlapping region, and calculating a motion vector between the pixels; fusing the optical flow information obtained under each scale, and constructing a globally consistent optical flow vector field; according to the fused optical flow vector, calculating to obtain a geometric transformation matrix of the whole overlapping region; and after image transformation and alignment are completed through the transformation matrix, fusion processing is carried out on overlapped areas. And the unification of the visual effect and the spatial integrity of the spliced image is ensured.

Base station antennas include a reflector, a first linear array of radiating elements extending forwardly from the reflector, the radiating elements in the first linear array configured to operate in a first frequency band, and a second linear array of radiating elements extending forwardly from the reflector, the radiating elements in the second linear array configured to operate in the first frequency band. These antennas further include an isolation wall that is positioned in between the first linear array of radiating elements and the second linear array of radiating elements. This isolation wall comprises at least one partially reflective surface that is configured to reflect, on average as a function of frequency, between 20% and 80% of incident radiation in the first frequency band.

The invention relates to the technical field of array antennas, in particular to a sparse linear array beam forming method and system with unit rotation and mutual coupling considered. Array elements are obtained, Euler angle derivation is carried out on rotation of the array elements around the z axis, and a radiation electric field after rotation is obtained; setting a beam forming target according to the electric field of the radiation field; according to a beam forming target, optimizing the position, the rotation angle and the excitation phase of an array element by adopting a random optimization algorithm, and generating an initial beam scheme; an active directional diagram is extracted from the initial beam scheme according to full-wave simulation, the active directional diagram is combined with a CAL-SAPSO algorithm to optimize the array element position, the rotation angle and the excitation phase, and a final array element directional diagram is generated; according to the invention, by introducing array element rotation and considering a mutual coupling effect, efficient beamforming of the sparse wiring array is realized, the radiation characteristic of the antenna array can be adjusted more finely, the peak side lobe level and the cross polarization level are effectively reduced, and the overall performance of the system is improved.

The invention discloses an overlay error real-time correction system based on image recognition and dynamic fitting, and belongs to the field of semiconductor advanced packaging and manufacturing. The system comprises an image acquisition module, an error decoupling module and a dynamic compensation module to form closed-loop control. The image acquisition module adopts a high-resolution linear array CCD (Charge Coupled Device) and an AI (Artificial Intelligence) algorithm to realize high-speed and high-precision mark identification and error quantization; the error decoupling module performs decoupling and trend prediction on system errors and random errors by using PCA, a region weight matrix, LSTM and the like; and the dynamic compensation module realizes mu s-level partition graph compensation by a DMD optical system based on an error thermodynamic diagram through heterogeneous platform operation. The system supports the real-time processing of more than 100Hz, the average overlay error is less than or equal to 0.3 mu m and the response time is less than 5 seconds in the FOPLP and other processes, the problems of lag, low precision and the like of the traditional method are effectively solved, and the manufacturing yield and the production line efficiency are remarkably improved.

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 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 field of underwater sound signal processing, in particular to an underwater DOA (direction of arrival) estimation method based on graph nerves and a convolutional neural network, which comprises the following steps: 1, establishing a linear array, and enabling narrow-band signals to simultaneously reach an underwater sound array; 2, performing signal preprocessing to obtain a signal covariance matrix, and performing normalization processing; 3, extracting correlation between array elements and spatial features of array signals, and performing data supplementation on sparse linear array information; 4, forming a double-branch structure, enhancing the information aggregation capability, and extracting features from a space path and a time domain path; and 5, constructing an adjacent matrix, filling node features of damaged array elements, adopting a double-branch structure, extracting spatial features and time domain features, carrying out feature integration, and outputting a DOA estimation result. The spatial correlation between array elements is extracted and the array sparsity problem is processed by using the graph neural network, and the time domain features of the signals are extracted in combination with the convolutional neural network, so that more accurate and more robust DOA estimation can be realized under the conditions of low signal-to-noise ratio and array sparsity.

The invention provides a calibration method suitable for a single-camera high-precision scanning system. The calibration method comprises the following steps: S1, carrying out area array mode calibration on a linear array camera; by adjusting the exposure time of the line-scan digital camera, moving the base station to image at different positions of the checkerboard, and solving internal parameters and distortion parameters of the line-scan digital camera; s2, line scanning mode calibration; performing line scanning imaging on the checkerboard to obtain pixel coordinates of angular points of the checkerboard; and separating the actual rotation angle of the checkerboard from the projection error, and further deducing the rotation angle of the real camera, thereby completing calibration. According to the calibration requirement of the linear array camera in a scanning imaging scene, a two-stage algorithm combining area array mode modeling and linear scanning imaging correction is provided, and the problem that a traditional calibration method is not applicable under the condition that a single camera is fixed and moves in a single direction is effectively solved.

The invention specifically discloses a satellite navigation interference source positioning method based on a distributed unmanned aerial vehicle, and relates to the technical field of satellite navigation interference resistance. According to the method, firstly, an unmanned aerial vehicle cluster carrying uniform linear array antennas is deployed in a target area, and after parameters of a system are initialized, all unmanned aerial vehicles synchronously collect interference signals and estimate the direction of arrival (DOA) of an interference source; then performing dual-domain interference suppression based on DOA information, calculating an interference source coordinate in combination with an unmanned aerial vehicle coordinate, and performing noise reduction; planning a path by using a path planning model of a multi-modal Markov decision process and a reinforcement learning algorithm to realize tracking of a dynamic interference source; and finally, judging and outputting a final coordinate through Euclidean distance convergence. The method solves the problems of limited coverage of a traditional fixed monitoring station and insufficient positioning precision of a single unmanned aerial vehicle, can efficiently position dynamic and multiple interference sources, improves the positioning precision and the anti-interference capability, reduces the deployment cost, and is suitable for satellite navigation interference source positioning in a complex environment.

The invention discloses a railway freight car loading state detection method based on image recognition, and particularly relates to the field of railway freight car loading state detection.The method comprises the steps that in the process that a railway freight car passes through an image collection area, carriage image frames are collected at a constant frame rate through linear array imaging equipment; an image sequence is constructed based on the wheel axle displacement, the acquisition timestamp and the frame number, the image sequence comprises a main image frame, a relay interpolation frame and an interpolation pseudo frame, and the main image frame, the relay interpolation frame and the interpolation pseudo frame are arranged to support time domain modeling of subsequent state response changes. The dynamic capture and accurate identification of the instantaneous abnormal state of the loading structure are realized by constructing an anchor point tracking graph and combining a time sequence rhythm modeling and similarity discrimination mechanism of a disturbance behavior. The problems that in the prior art, an identification system lacks the space-time continuous modeling capacity for the short-time change state, and an abnormal judgment blind area and misjudgment distortion exist are solved.

The invention discloses a multi-mode control method for voice interaction and gesture recognition of an atmosphere lamp, and belongs to the field of intelligent control. The method comprises the following steps: S1, constructing a multi-mode sensing module, and arranging a bidirectional linear array microphone, a dual-lens infrared depth camera and a space calibration laser transmitter; s2, extracting voice features by adopting an extended spectrogram domain attention network, and generating a first semantic vector; s3, dynamic attitude features are extracted through the time-airspace sparse convolutional network; s4, performing modal alignment and cooperative coding on the two semantic vectors based on a bidirectional gating fusion mechanism, generating a joint interaction instruction vector, and solving modal conflicts by a time sequence synchronization and confidence regulation strategy; s5, inputting the vector into a multi-task intention classifier, analyzing a user intention and outputting a unique atmosphere lamp control code; and S6, transmitting the control code to a driving unit to realize light control. The beneficial effects are that voice and gesture fusion control is realized, and atmosphere lamp interaction intelligence and response precision are improved.

The invention relates to the technical field of industrial visual inspection, and discloses an asynchronous exposure double-view-field imaging system and method, and the system can achieve the merging of independent view field images collected by two parallel lenses into a single linear array camera through a reflector and a non-polarization beam splitting cube based on the asynchronous exposure double-view-field imaging system. Meanwhile, in cooperation with precise time sequence control, the system drives two independent stroboscopic light sources to be synchronous with line trigger signals of the camera, different view fields are alternately illuminated in odd-even line periods, the camera is made to perform asynchronous exposure, and two paths of image information are collected in a time-sharing mode; and finally, reconstructing two complete, independent and seamlessly spliced detection images from the interleaved data streams through an odd-even line separation algorithm. According to the invention, with the hardware cost of a single camera, the detection visual field is obviously expanded, the detection efficiency and the image geometric precision under the high-line scanning frequency are ensured, and the excellent balance of the performance, the cost and the efficiency is realized.

A hybrid scanning antenna including: a reflector having a focal line; a first mechanical movement to move the reflector about a first axis; a second mechanical movement to move the reflector about a second axis; a linear array fixedly disposed along the focal line to electronically scan at a scan angle about a third axis; and a controller to control the first mechanical movement, the second mechanical movement and the scan angle of the linear array to orient the hybrid scanning antenna to a look angle of a remote transceiver.

The invention discloses a roll-to-roll laser cutting equipment online alignment and trajectory interpolation control method, which relates to the technical field of laser cutting alignment and trajectory interpolation, and comprises the following steps: marking a reference point for an adhesive film; monitoring a datum point on the surface of the adhesive film through a linear array camera and recording datum data; analyzing the deformation data and the displacement data through the reference data; performing compensation alignment on the cutting head based on the deformation data and the displacement data to obtain a compensation cutting point, and controlling the cutting head to start cutting from the compensation cutting point; a cutting shape is obtained, the interpolation track of the cutting head is analyzed through an interpolation algorithm, and the cutting head is controlled to cut the adhesive film according to the interpolation track; the method is used for solving the problems that according to an existing laser cutting alignment and track interpolation technology, alignment cannot be conducted on a product in advance, compensation for deformation of the product is lacked, the laser cutting efficiency is low, and the size error of the cut product is large.

One or more implementations of the present disclosure relate to a wireless, multi-sensor food thermometer that includes a temperature probe having a linear array of temperature sensors that are operative to measure: temperature profiles within a food product during a cooking process, a temperature at a core of the food product, a temperature at the surface of the food product, and a temperature of the ambient cooking environment in which the food is being cooked. The temperature probe includes a wireless interface that transmits temperature information to one or more external devices for use thereby. Rather than transmitting raw temperature data, the temperature probe may transmit coefficients that correspond to a function that describes the temperature profile within the food or parameters necessary to solve a governing heat equation, which reduces data transmission requirements and reduces power consumption. The food thermometer may include a charger case that houses the temperature probe and charges a power source of the temperature probe using a replaceable battery.

The invention relates to the technical field of artificial intelligence and electronic manufacturing detection, and discloses an ultra-thin printed circuit board defect detection method and system based on AI vision. The objective of the invention is to solve the technical problems of poor imaging quality, high missed detection and false detection rate, weak generalization ability and difficulty in consideration of efficiency and precision caused by buckling deformation, surface reflection and tiny and diverse defects of an ultrathin printed circuit board in the existing detection technology. The method comprises the following steps: performing multi-angle image acquisition through a composite illumination module and a high-resolution linear array camera; geometric correction is carried out on the image by using an SIFT algorithm and affine transformation, and the image contrast is enhanced by using a CLAHE algorithm; generating a defect candidate region through the lightweight full convolutional network; and carrying out accurate classification and identification on the candidate region by using a deep residual network. According to the technical scheme, high-precision, high-efficiency and low-false-alarm detection of the tiny defects of the ultrathin printed circuit board can be achieved, and the automation level and adaptability of detection are remarkably improved.

The invention belongs to the technical field of photoelectric displacement precision measurement, and particularly provides a cylindrical grating scribed line error detection method and system. The method comprises the following steps: firstly, calibrating a cylindrical grating positioning error through two angles, then collecting gray scale data of the cylindrical grating at different measurement angles according to a point light source and a linear array image sensor to calculate a non-coaxial error, and finally removing the non-coaxial error from the positioning error to realize the detection of the scribing error. According to the invention, interference of non-coaxial errors in a scribed line error detection process can be effectively suppressed, the inherent precision of cylindrical grating displacement measurement is effectively improved, and the method is suitable for industrial, aerospace and other scenes with high precision requirements.

The invention provides a single-bit digital beam forming method and device, a terminal and a medium, and relates to the technical field of radar detection and wireless communication, and the method comprises the steps: constructing a target single-bit low-rank matrix recovery model of a uniform linear array comprising a plurality of sensors, and an optimization problem of the target single-bit low-rank matrix recovery model, and converting the target single-bit low-rank matrix recovery model into a processable problem, solving and determining a low-rank matrix by using a near-end block coordinate descent algorithm and a gradient descent algorithm, calculating an estimated covariance matrix of a target and an interference signal based on the matrix, performing eigendecomposition to calculate an interference subspace, and modeling a single-bit digital beam forming problem according to a base vector of the interference subspace to obtain a single-bit digital beam forming matrix. And calculating a closed-form solution based on the principle that the optimal solution is located in the orthogonal complement space of the interference subspace. According to the method, the resolution requirement of the analog-to-digital converter can be reduced by fully utilizing the single-bit quantization characteristic, and the beamforming performance of single-bit digital beamforming is close to that of high-bit digital beamforming when the hardware complexity and the power consumption are reduced.

The invention provides a DOA (direction of arrival) estimation method and device of a random linear array. The DOA estimation method comprises the following steps: establishing an observation model representing a relationship between direct output of a sensor unit and a source signal, and carrying out compressed sampling on the direct output of each unit of the linear array by applying a random demodulator; constructing a linear observation relationship between the sampling signal of the random demodulator and a decomposition coefficient matrix of the source signal matrix on a certain sparse base, obtaining an estimated value of the decomposition coefficient matrix by using a sparse sampling reconstruction method, and further reconstructing to obtain an estimated value of an original source signal matrix; and calculating the DOA spectrum and estimating the DOA angle of the target or source according to the peak value of the spectrum. According to the invention, the technical problems of low estimation precision and low resolution caused by large design difficulty of a sensor array, heavy data acquisition burden and single analysis signal are solved.

The invention relates to the technical field of industrial visual inspection, in particular to a high-speed enameled wire surface defect detection system and method. The real-time diameter measuring module adopts a laser diameter measuring instrument to measure the diameter of the enameled wire in real time, and the precision reaches + / -0.001 mm; the high-voltage pinhole detection module is used for verifying electrical defects; and a control processing module. Optimized combination of a high-speed line-scan digital camera and an optimized Yolo11 model is realized by innovatively fusing visual detection, triple technologies of optical measurement and physical detection are realized, full-dimensional quality verification and multispectral imaging are combined with the optimized Yolo11 model to improve the detection rate of 0.1 mm-level pinholes and scratches, and meanwhile, a laser diameter measuring instrument is integrated to feed back the line diameter in real time, so that the detection rate of the 0.1 mm-level pinholes and scratches is improved. In addition, high-voltage pinhole electrical defect signals are aligned with visual results in a space-time mode, and the problem of high-speed misinformation is solved.

The invention discloses glass defect layering detection equipment based on multiple imaging planes, and belongs to the technical field of glass defect detection.The glass defect layering detection equipment based on the multiple imaging planes comprises a conveying module, a scanning module and a fusion analysis module, and the scanning module is provided with a front linear array camera, a side surface area-array camera and a side edge area-array camera; layered acquisition is carried out on glass images, and the fusion analysis module can detect glass defects in the images and carry out fusion analysis on detection results of different imaging surfaces. Through the combined design of the front linear array camera, the side area-array camera and the side area-array camera with the fusion analysis method, glass images can be collected from multiple angles, fusion analysis is carried out on the collected images, the reliability of overall detection of glass defects can be improved, the false alarm rate can be reduced, repeated statistics can be avoided, and the detection efficiency is improved. And the detection result is more accurate.

The invention belongs to the technical field of fluorescence spectrum collection, and particularly relates to a quasi-three-dimensional fluorescence spectrum collection method and spectrograph based on a software phase locking technology, and the method comprises the following steps: respectively modulating coprime modulation frequencies of a plurality of laser light sources with different wavelengths, and synchronously exciting the laser light sources to the surface of a sample; a fluorescence signal generated on the surface of the sample is received and processed by a linear array CCD detector to obtain a multi-wavelength spectral signal simultaneously containing background light, scattered light and fluorescence components; inputting the multi-wavelength spectral signal into a software phase-locked amplification system, introducing a Costas ring to the software phase-locked amplification system for real-time tracking and correction, and generating a synchronous reference signal consistent with the laser modulation frequency; inputting the synchronous reference signal into a software lock-in amplifier, and extracting a plurality of pure fluorescence spectrum components excited by specific-frequency laser; and synthesizing the plurality of pure fluorescence spectrum components to finally form a quasi-three-dimensional fluorescence spectrum.

The invention discloses an airport runway foreign matter detection method based on a YOLO11 optimization network, and belongs to the technical field of target detection in computer vision and deep learning. The problems that detection challenges of multi-scale foreign matter features and complex background textures exist when a traditional detection model is used for detecting airfield runway foreign matter, the multi-scale features of millimeter-level bolts and centimeter-level gravel coexist easily cause missing detection, and the texture similarity of metal pieces and runway asphalt further reduces the detection precision are solved. According to the airport runway foreign matter detection network structure based on the YOLO11 optimization network and the detection method, the real-time image of a vehicle-mounted line-scan digital camera is obtained, and the FOD appearing in the image is detected.

The invention discloses a conveyor belt damage identification method and system based on X-ray detection, and relates to the technical field of image identification detection, and the method comprises the following steps: preparing three groups of orthogonally distributed X-ray equipment, starting three groups of orthogonally distributed X-ray sources to synchronously carry out penetration scanning on a running conveyor belt, and starting the X-ray equipment; three-dimensional perspective images of the top, the left side and the right side of the conveying belt are acquired through a linear array detector, meanwhile, an MEMS three-axis acceleration sensor is started to acquire vibration data of the conveying belt in real time, the vibration displacement is calculated, sub-pixel-level geometric correction is carried out based on the vibration displacement, and image offset errors caused by vibration are eliminated. The three groups of orthogonally distributed X-ray sources are arranged for synchronous scanning, and the vibration data are combined to perform offset correction on the image, so that the effect of eliminating the vibration interference during the high-speed operation of the conveying belt is achieved, the stable and clear image is obtained under the dynamic scene of the high-speed operation of the conveying belt, and the detection error caused by poor image quality is reduced.

The invention relates to the field of concrete beam damage detection, in particular to an ultrasonic imaging method for concrete beam damage detection, which comprises the following steps: arranging a linear array comprising a plurality of ultrasonic probes on a single-side contactable surface of a to-be-detected concrete beam; preparing a multi-channel ultrasonic transmitting and receiving instrument, and connecting the multi-channel ultrasonic transmitting and receiving instrument with the sensor array and the control computer; a multi-channel ultrasonic device is used to work in a full-matrix capture mode, in the full-matrix capture mode, each probe is sequentially excited to emit ultrasonic pulses, and time domain signals received by all the probes of the array are synchronously recorded to obtain an original full-matrix data set. According to the method, strong scattering noise generated by the concrete aggregate can be inhibited through the deep convolutional neural network so as to improve the signal-to-noise ratio of the image, the sound velocity field conforming to the non-uniform characteristic of the concrete can be constructed through local sound velocity inversion and iteration based on the strong reflection points, the problems of imaging artifacts and form distortion are avoided, and the accuracy of the imaging process is improved. And the concrete damage detection precision is ensured.