392 results about "Gaussian filter" patented technology

The invention relates to the technical field of image detection, in particular to a PCBA board defect detection method and system based on image processing, and the method comprises the following steps: carrying out the meshing calculation of a gray scale deviation after a gray scale image is subjected to Gaussian filtering denoising, generating change rate data, carrying out the statistics of a frequency number, constructing a histogram, combining with an Otsu algorithm, and generating a candidate mask; extracting pixels based on a mask, calculating a gradient modulus, screening edge candidate points, carrying out gradient direction connection and morphological processing to generate a complete edge structure, expanding a connected domain through a region growing algorithm, aligning the connected domain with a template contour, and outputting defect coordinates. According to the method, the defect identification sensitivity is improved through combination of gray level image gridding processing and dynamic threshold calculation, a candidate mask is generated through grid gray level change rate statistics and an Otsu algorithm to avoid over-segmentation missing detection, and the contour precision is improved through combination of gradient modulus difference screening and morphological closed operation optimization. The region growing algorithm and template dynamic alignment reduce deformation misjudgment, and staged dimension reduction and feature enhancement reduce calculation complexity and solve resource waste.

The invention discloses a mobile terminal streetscape image real-time segmentation method based on a lightweight neural network, and relates to the technical field of image segmentation. The method comprises the following steps: firstly, carrying out 320 * 320 adjustment, Z-score standardization, adaptive histogram equalization and 3 * 3 Gaussian filtering preprocessing on an input streetscape image; then, an improved MobileNetV3 backbone network is used, and a five-scale feature map is output in combination with DropBlock regularization through eight feature extraction stages including depth separable convolution and an SE attention module; multi-scale features are fused through a U-shaped structure, and a fusion feature map is generated through up-sampling, element-by-element addition of dimension reduction low-layer features and an attention gating module; and during reasoning, outputting a segmentation mask by using a convolutional layer, Softmax and a conditional random field, and finally performing knowledge distillation, weight pruning, 8-bit quantization and TensorRT optimization. According to the invention, high-precision real-time street view segmentation is realized, the robustness is high, and the method is suitable for different devices and scenes.

The invention discloses a pavement crack detection method based on a Yolov8 model, and belongs to the technical field of crack detection. Comprising the following steps: constructing a pavement crack segmentation data set: acquiring a road crack image through an unmanned aerial vehicle, performing data enhancement processing of geometric transformation and color transformation on the image in combination with a public data set, performing Gaussian filtering denoising on a noise image, and performing crack labeling by using Label; based on a YOLOv8-Seg model, improvement is carried out by introducing a PKIblock multi-scale convolution kernel, generalizing an efficient layer aggregation network GELAN module, an EMA attention mechanism and replacing a spatial pyramid pooling layer SPPF into a SimSPPF, and a road crack recognition model YOLOv8-RCI is constructed; and performing crack detection and instance segmentation on the unmanned aerial vehicle image by using the trained YOLOv8-RCI model, and outputting a crack position and mask information. Through the improvement in the four aspects, the detection segmentation performance of the model is improved, and the model meets the requirement of real-time detection.

The invention discloses a dynamic Gaussian filtering point cloud optimization method and system based on local features, and relates to the technical field of point cloud data processing and three-dimensional computer vision, and the method comprises the steps: carrying out the preprocessing of original laser radar point cloud data, dynamically adjusting the sampling rate, constructing an index structure, and carrying out the dynamic Gaussian filtering based on the preprocessed point cloud data. The method comprises the steps of extracting geometric features of local neighborhoods, fusing the geometric features into weighted feature vectors, dynamically adjusting kernel function parameters of Gaussian filtering according to the weighted feature vectors, generating an adaptive filtering window, performing weighted Gaussian filtering processing on point cloud data by using the dynamically adjusted kernel function parameters, and outputting denoised point cloud data. According to the method, efficient point cloud retrieval is realized through dynamic sampling and index construction, multi-feature optimization weight is fused to generate an adaptive filtering window, de-noising and detail reservation are balanced, spatial distribution is recovered, missing points are interpolated and filled, a complete point cloud data set is generated, and data quality and processing precision are remarkably improved.

The invention discloses a power equipment defect intelligent identification method, system and device and a medium, and the method comprises the steps: collecting an original image of power equipment, and screening the original image of the power equipment to obtain a channel image; carrying out enhancement processing on the channel image and then calculating a gray scale difference value to obtain a gray scale image; converting the gray level image into a frequency spectrum image by adopting fast Fourier transform, and constructing a Gaussian filtering function to carry out convolution and inverse transformation on the frequency spectrum image to obtain a spatial domain image; and performing adaptive threshold segmentation on the spatial domain image, dividing the image into a defect area and a non-defect area, obtaining a segmented image, and performing morphological processing on the segmented image to obtain an electrical equipment defect identification result. According to the method, the problem of aliasing in traditional spatial domain processing is solved, the defect area is accurately extracted, short-time interference and real defects can be effectively distinguished, and the segmentation accuracy is improved.

The invention belongs to the technical field of inspection robot systems, and particularly relates to an inspection robot path planning method and system.The visual semantic perception module collects an industrial environment image through a top industrial camera, after graying and Gaussian filtering preprocessing, feature points are detected and matched through an ORB algorithm, and a path planning result is obtained; in combination with an illumination self-adaptive threshold screening mechanism, mismatching points are eliminated, semantics are marked, and a semantic feature map is constructed; the initial path planning module generates an initial path through a semantic cost-containing A * algorithm based on the map; the dynamic obstacle avoidance module captures a moving obstacle by using a visual sensor, and predicts a trajectory through Kalman filtering; the path optimization module combines an initial path and an obstacle track, optimizes the path by using quadratic programming of a fusion curvature constraint and an energy consumption model, and corrects positioning by fusing vision and IMU data through a dynamic weight fusion algorithm; and the execution feedback module generates an instruction according to the optimized path, re-triggers path optimization, forms a closed loop, and ensures the inspection stability.

The invention relates to the technical field of image processing, in particular to an intelligent welding defect positioning and detecting system based on image processing. Obtaining a suspected noise degree according to gradient features and neighborhood gray level distribution features of pixel points in the welding image; obtaining a gray confidence coefficient according to the gray features of the pixel points and the gray difference features of the pixel points and the neighborhood; obtaining a structure confidence coefficient according to the area feature of the connected domain where the pixel point is located, the contour change feature of the edge line of the connected domain and the gradient distribution feature of the edge line in the normal direction; obtaining a denoising coefficient of the pixel point according to the suspected noise degree, the gray level confidence coefficient and the structure confidence coefficient; and adjusting the standard deviation in the Gaussian filtering algorithm according to the de-noising coefficient. The method comprises the following steps: denoising a welding image according to an adaptive standard deviation, and performing image enhancement on the denoised welding image to obtain a to-be-detected image; welding flaw detection is carried out on the to-be-detected image, and the detection accuracy is improved.

The invention relates to the technical field of intelligent monitoring, in particular to a municipal building engineering construction progress monitoring method based on an unmanned aerial vehicle and laser scanning, and the method comprises the following steps: obtaining point cloud data through carrying laser scanning by the unmanned aerial vehicle, calibrating a space coordinate, binding a design coordinate system, carrying out Gaussian filtering denoising processing, and analyzing point cloud through coordinate transformation. The method comprises the following steps: extracting a main axis vector by adopting a principal component analysis method, dividing a sectioning region, calculating a cosine value of a normal included angle, classifying and identifying a difference region by Euclidean distance, carrying out secondary scanning clustering segmentation, and calculating an offset judgment state label set. According to the method, a three-dimensional reference is established by adopting laser scanning and coordinate calibration, Gaussian filtering is combined to eliminate noise, PCA is used to extract a geometric main axis, a normal included angle matching degree is calculated, Euclidean distance classification detection deviation is carried out, geometric difference is quantitatively identified, graph-model matching precision is improved, millimeter-level capture is realized, an automatic monitoring system is constructed, and manual errors are reduced. And controlling the power-assisted progress.

The invention discloses an infrared image denoising method and system based on artificial intelligence, and relates to the technical field of image denoising, and the method comprises the steps: collecting infrared image data, extracting frequency domain spatial features, carrying out Gaussian filtering, restoring the features to an image space, determining a high-frequency feature map, extracting image background information, and determining a low-frequency feature map. And splicing the high-frequency feature map through a generator, and carrying out two-dimensional transpose convolution operation by adopting an encoder based on a convolution transpose self-attention mechanism. According to the method, the frequency domain of an infrared image is converted into high-frequency and low-frequency characteristic decomposition, high-frequency characteristics are extracted based on a Gaussian high-pass filter, space structure information such as edges and textures can be effectively reserved, the importance of the high-frequency characteristics can be weighted through an attention mechanism, exploration of noise can be enhanced, and for the background part of the image, the resolution of the image is improved. The attention module can identify an area with excessive brightness fluctuation, and can synchronize transmission of secondary features in a noise removal process through residual features.

The invention discloses a semiconductor processing defect detection method based on artificial intelligence, and relates to the technical field of semiconductor processing defect detection. The method comprises the following steps: acquiring wafer position coordinates, an optical image and process equipment dynamic parameter data; generating a microdefect feature matrix by using a multi-scale Gaussian filtering model and an asymmetric convolution algorithm; converting the process parameters into a position-associated process parameter matrix through space-time mapping and a correlation matrix algorithm; performing radial pooling and multi-expansion-rate cavity convolution algorithms on the fused feature tensor, and extracting global and local feature vectors; generating a defect probability distribution diagram by adopting a polar coordinate dynamic weighted fusion algorithm; and outputting the defect coordinates and the type identifier through an adaptive threshold algorithm, and generating a process equipment control signal according to predefined logic. Through an innovative multi-source data fusion calculation model, accurate defect identification and process closed-loop control are realized, and the detection precision and efficiency are improved.

The invention discloses an ultra-low illumination-oriented SPAD active imaging data compression method, which comprises the following steps of: performing wavelet decomposition on histogram data of a single pixel in a time-frequency domain based on wavelet transform to obtain low-frequency data of each pixel after wavelet decomposition; by taking each pixel as a center, performing non-maximum suppression and data enhancement on the low-frequency data after the wavelet decomposition of the current pixel by using adjacent pixels to obtain processed compressed data; customizing different Gaussian kernel parameters for each pixel according to the possibility that each pixel is located at the boundary, and performing Gaussian filtering on the processed compressed data of each pixel to obtain filtered data; and performing depth estimation on the filtered data to obtain a final depth image. According to the method, the depth reconstruction performance of the laser pulse can be improved by utilizing the multi-resolution characteristic of wavelet transform, the space-time correlation of signal photons and the smoothness of Gaussian filtering.

In a dynamic environment, a visual SLAM (Simultaneous Localization and Mapping) system often causes the problems of large positioning error and inaccurate map construction due to dynamic target interference. In order to improve the robustness and precision of the system, the invention provides a dynamic scene SLAM optimization method based on improved YOLOv11 and geometric consistency constraint. Firstly, ORB features in a scene are extracted, and meanwhile a prior dynamic object and feature points on the prior dynamic object are removed through a YOLOv11 semantic segmentation model; secondly, eliminating feature points on the potential dynamic object by utilizing geometric consistency constraint, and recovering a background shielded by the dynamic object through a semantic perception Gaussian filter; and finally, selecting a high-quality key frame and applying the key frame to loopback detection and global optimization, constructing a basic Gaussian graph through a group of determined poses and point clouds, and finally fusing repair frame information to realize new view rendering and three-dimensional scene optimization.

The invention relates to an underwater image quality improvement method based on adaptive color correction and contrast enhancement, which comprises the following steps: firstly, designing an adaptive correction strategy to carry out channel compensation on an underwater image to obtain an underwater image after color correction; a brightness channel is extracted, and a color interference layer is filtered out, so that global backscattered light is estimated; and gradient domain detail enhancement is carried out, defogging processing is carried out on the base layer of the underwater image after color correction, brightness adjustment is carried out on uneven illumination, and an enhanced image is obtained. According to the invention, by compensating the attenuation of the underwater environment to the image information, the color distribution balance of the three channels is realized, so that the color channels of the underwater image are naturally distributed; a plurality of prior knowledge of the back scattering light is fused, a Gaussian filter of an adaptive standard deviation is constructed, a color interference layer is separated, and the back scattering light can be accurately estimated without being interfered by a white object and a highlight area; therefore, multi-target-oriented contrast enhancement is realized to improve the overall visibility of the image.

The invention discloses a deformation monitoring method and system based on a machine vision technology, and relates to the technical field of crossing of structural health monitoring and computer vision, and the method comprises the steps: improving the angular point positioning precision in a calibration stage through a sub-pixel-level Harris angular point detection algorithm; robust recognition and initial positioning of an artificial marker in a first frame of image are realized by using an X-Feat convolutional neural network model, an anti-interference template is generated in combination with histogram equalization and Gaussian filtering, the stability of the system is enhanced, a local search area is delimited by taking an initial positioning point as a center, and a deep learning and interpolation algorithm is combined, so that the anti-interference performance of the system is improved. High-precision tracking and displacement calculation of the marker center are realized, the monitoring resolution is effectively improved, pixel-level displacement is accurately converted into physical space displacement through inverse transformation of a homography matrix, and dual threshold criteria of mode length and change rate are introduced for dynamic early warning, so that the engineering practicability and safety response capability of a monitoring result are improved.

The invention relates to the technical field of industrial defect detection, and discloses an aluminum surface micro defect detection system for optimizing an RT-DETR model in combination with an attention mechanism, which comprises an image acquisition module, an image preprocessing module, an improved RT-DETR detection module and a result output module. According to the aluminum surface micro-defect detection system for optimizing the RT-DETR model in combination with the attention mechanism, small target copying is executed through an image preprocessing module to increase micro-defect samples, defects and matching backgrounds are fused through image splicing, illumination is optimized through brightness adjustment, Gaussian filtering noise reduction is conducted, and original defect information is strengthened; an orthogonal attention feature extraction module in the improved RT-DETR detection module strengthens channel features, a multi-scale deformable frequency hierarchical attention coding module processes the features according to high and low frequencies, an ELAHS-FPN feature fusion module fuses the multi-scale features, a super-resolution auxiliary branch improves the feature resolution, a prediction frame is corrected in combination with an Inner-GIoU bounding box loss function, and the detection precision is improved. And weak characteristics such as paint bubbles, scratches and jet flow are effectively extracted.

The invention discloses an ultra-high-definition video stream adaptive coding method based on deep learning visual saliency, and the method comprises the steps: carrying out the five-scale Gaussian filtering processing and image pyramid construction of a video frame, and combining Sobel gradient, Laplacian edge and local binary pattern feature extraction to generate a multi-scale feature map; a pre-training saliency detection network is adopted, and a smooth saliency thermodynamic diagram is generated through processing of a feature adaptation layer, a residual encoder, a self-attention mechanism and a transposed convolution decoder; dividing the video frame into a high region, a middle region and a low region according to the saliency thermodynamic diagram, and establishing a regionalization coding parameter table; performing differentiated prediction modes, motion estimation and quantization strategies on different salient regions; and organizing coded data according to an H.265 / HEVC standard, and embedding the saliency thermodynamic diagram into supplementary enhancement information for transmission. According to the method, the important region concerned by the user can be intelligently identified, a differentiated coding strategy based on content semantics is realized, and the coding efficiency is remarkably improved.

The invention discloses a method for determining brain PET standardized reconstruction parameters based on clinical brain MRI and PET data, and relates to the field of medical image processing. The method comprises the following steps: firstly, collecting clinically paired MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) images for preprocessing, carrying out virtual reconstruction by combining a partial volume correction algorithm and system parameters of equipment to estimate'real 'brain activity distribution so as to obtain a first simulated PET image, and calculating difference or similarity between the first simulated PET image and a clinical PET image; through multiple iterations, obtaining an optimal'real 'brain activity distribution diagram, and processing the optimal'real' brain activity distribution diagram into a'standardized PET image 'according to standardized Gaussian filtering; then, constructing different candidate combinations of reconstruction parameters, inputting an optimal'real 'brain activity distribution diagram, traversing a second simulated PET image generated by simulation reconstruction under each combination, and respectively performing difference or similarity calculation with the'standardized PET image', so as to obtain an optimal'real 'brain activity distribution diagram; and selecting the group of candidate reconstruction parameters with the minimum difference or the highest similarity as final standardized reconstruction parameters. The method gets rid of dependence on a physical motif.

The invention discloses a submarine observation network power supply system protection method based on traveling wave extreme value time, and relates to the technical field of submarine power grid protection, and the method comprises the steps: determining the layout position of a protection device according to a network structure, a power supply path and node importance, and dividing a system into protection regions defined by adjacent protection devices; voltage signals are collected in real time, first-order differential processing is carried out, and fault occurrence is judged by comparing the voltage change rate with a preset threshold value; recording current traveling wave data before and after the fault and carrying out Gaussian filtering noise reduction; calculating the extreme value time of the traveling wave by using a differential gradient algorithm; comparing the time difference of traveling wave extreme values on two sides of the protection area to judge the fault direction; judging a fault area in combination with the extreme time and a preset threshold value; and when the forward direction criterion and the intra-region criterion are met at the same time, a protection action is triggered. The method has the advantages that high-reliability fault positioning and rapid isolation can still be achieved under the communication failure scene, and the problem of remote fault and high-resistance fault recognition is effectively solved.

The invention belongs to the technical field of scanning measurement, particularly relates to a cable outer diameter and ovality measurement method and system based on line laser contour scanning, and aims to solve the technical problem of low measurement precision caused by the fact that an existing algorithm is sensitive to noise, asymmetry of light strips and contour discrete points. The measurement method comprises the following steps: S1, performing anisotropic Gaussian filtering on a pixel according to a local tangential direction of a preliminary center line; s2, calculating a second derivative value and a third derivative value of the sub-pixel center point in the normal direction; s3, performing initial weighted least square ellipse fitting by using the corrected contour point set and the corresponding fitting weight to obtain parameters of an initial ellipse; and S4, based on the lengths of the long axis and the short axis of the fitting ellipse, calculating to obtain the outer diameter and the ellipticity of the cable. According to the measurement method, the interference of abnormal data points introduced by local flaws such as oil stains and scratches on the surface of the cable is eliminated, and the accuracy and the stability of a fitting result are improved.

The invention discloses a method for detecting tea saponin in a tea extract, and relates to the technical field of chemical analysis and computer science, and the method comprises the following steps: eliminating high-frequency noise of tea extract data by using a Gaussian filtering algorithm, eliminating baseline drift by using a baseline correction algorithm, and generating a tea extract sample data stream; the method comprises the following steps: performing dynamic gradient separation through a chromatographic signal simulation algorithm on the basis of a tea extract sample data stream to generate a virtual chromatographic peak sequence, calculating a peak area through a sliding window integration algorithm on the basis of the virtual chromatographic peak sequence, and generating chromatographic signal data by using a wavelet transform threshold denoising algorithm, the chromatographic signal data are input into the deep convolutional neural model, the feature pyramid network is obtained through the multi-scale feature fusion algorithm, the feature information of the chromatographic peaks is extracted from different scales by constructing the deep convolutional neural model and combining the multi-scale feature fusion algorithm, and therefore the recognition precision of the feature peaks is improved.

The invention discloses a friendly charging pile management system and method for a pure electric vehicle, and relates to the technical field of charging piles. Comprising a sensing layer, a cloud processing layer and a user interaction layer which realize data interaction through an encrypted communication protocol. The vehicle type identification module receives three frames of images of entering vehicles collected by a wide-angle camera, after Gamma gray correction, 3 * 3 Gaussian filtering and zooming to 224 * 224 and normalization preprocessing, vehicle types are classified, pure electric vehicles enter a priority queue of the double-queue scheduling module, and mixed extended-range electric vehicles enter a lag queue; the double-queue module builds queues according to Redis Zset and ranks the queues every 5 seconds according to a priority rule, and when the charging piles are idle, scheduling with the highest priority is selected; an identity verification algorithm of the parking lock control module is used for unlocking through license plate editing distance comparison and 120-second time sequence verification, queue jumping is avoided, and the problems that pure electric charging resources are occupied and queuing is disordered are solved.

The invention provides a narrow-linewidth single-frequency laser frequency stabilization system and method based on a Gaussian filter, and the system employs the Gaussian filter to reduce the optical power of single-frequency laser, and employs the frequency selection characteristic of the Gaussian filter, the frequency spectrum of the single-frequency laser is a single narrow peak, and the frequency stability of the single-frequency laser is improved. When laser frequency passes through a designed Gaussian filter (the fluctuation range of a frequency-stabilized laser source is only on the left side of the center frequency of the Gaussian filter), laser signals can be reduced according to a Gaussian function rule, and along with frequency change, attenuation degrees are different and monotonically change, so that output optical power is different, and the output optical power is changed. And the optical power is compared with the optical power of a preset frequency stabilization target (a half-high point on the left side of a Gaussian filter) to obtain the deviation of the laser source relative to the target frequency stabilization frequency, so that the laser frequency is correspondingly adjusted to achieve the frequency stabilization purpose. The high-sensitivity optical power detection is realized, the response speed and the frequency stabilization precision of the system are improved, and the system is simple in structure and low in maintenance cost.

The invention provides a control system for suppressing peak of output voltage and current of a power amplifier. The system comprises a PS signal processing module, a PL signal processing module and a high-voltage power circuit, wherein the PS is configured to receive a bus signal issued by an upper computer, execute digital Gaussian filtering processing on the bus signal and then transmit the bus signal to the PL signal processing module; meanwhile, relevant data of the power amplifier are collected and uploaded; the PL signal processing module is configured to receive a signal which is issued by the PS and is subjected to digital Gaussian filtering processing, and pre-process the signal; and transmitting the processed signal to a high-voltage power circuit to control the high-voltage power circuit to execute power conversion operation on the load. According to the method provided by the invention, smooth transition is realized at the phase jump position of the signal by adjusting the parameters of the digital Gaussian filter, so that the peak of the output voltage and current of the power amplifier is inhibited, the stress of a post-stage high-voltage power circuit device is reduced, the post-stage output power is effectively improved, and the stability of the power amplifier is improved.

The invention provides an intelligent grid size field prediction method, which comprises the steps of inputting geometric data, material parameters and boundary conditions of a to-be-processed CAE model, automatically extracting an input feature set, inputting the input feature set into a trained hybrid model, and outputting an initial grid size field; and performing post-processing on the initial grid size field, including performing smoothing processing by adopting Gaussian filtering to ensure that the size difference of adjacent patches does not exceed 20%, executing constraint verification to ensure that the grid size is within a preset range, and performing multi-physical field adaptation to take the minimum value of the size required by each physical field. And generating a CAE (Computer Aided Engineering) grid based on a post-processing grid size field driving grid generation algorithm, feeding back the grid quality in real time in a grid generation process, and if the grid distortion rate exceeds 5%, predicting and adjusting the size again. According to the method, the grid generation efficiency is remarkably improved, the manual intervention cost is reduced, the simulation precision and stability are ensured, and the method is suitable for complex engineering analysis scenes in the industries of aerospace, automobiles and the like.

The invention discloses a Fourier finite difference deconvolution imaging method based on inclination angle adaptive noise suppression, and relates to the technical field of geophysical exploration of petroleum. The method specifically comprises the following steps: designing a scattering point speed model to solve a partial point spread function of an underground space; solving a partial point spread function by a speed disturbance method; solving point spread functions of all positions of the underground space by carrying out Gaussian filtering technology and inverse distance weighted interpolation on part of the point spread functions; using a plane wave destruction method to solve the inclination angle attribute of the work area, and constructing an inclination angle self-adaptive diagonal diffusion Fourier finite difference migration method based on the inclination angle attribute to solve a high-quality initial image suitable for an inversion system; and performing multi-dimensional deconvolution in the wave number domain to obtain a final migration imaging result. According to the method, the point spread function is successfully introduced into Fourier finite difference imaging, and the resolution and deep amplitude of a conventional Fourier finite difference imaging method can be effectively improved.

The invention discloses a weed detection method and device used in a complex environment background, and the method comprises the steps: carrying out the simplification of channel attention convolution and channel self-attention convolution in a smooth U-shaped network; a space channel convolution attention module is designed by introducing a space attention branch, a gating mechanism and two different residual connection modes, an edge filtering and Gaussian filtering combined mechanism is proposed based on an edge Gaussian aggregation module in a linear efficient Gaussian network, and rapid spatial pyramid pooling is fused. A multi-scale pooling strategy, a channel attention mechanism and a dynamic weight distribution mechanism are introduced, a multi-scale pyramid pooling hyper-edge Gaussian aggregation module is designed, a context consistency strategy is constructed on the basis of a space enhancement feedforward module in a space enhancement multi-scale network, dynamic gating is designed through shielding masks, and a multi-scale pyramid pooling hyper-edge Gaussian aggregation algorithm is constructed. Designing an efficient multi-scale space enhancement network module; based on a YOLOv9t model, a space channel convolution attention module, a multi-scale fast pyramid pooling hyper-edge Gaussian aggregation module and an efficient multi-scale space enhancement network module are combined to construct a space enhancement type YOLO model; and weeds are detected based on the spatial enhanced YOLO model.

The invention relates to the technical field of image processing, in particular to a glass punching hidden crack detection method and system adopting image processing. Comprising the following steps: acquiring point cloud data of target glass, calculating the curvature of each region, and generating a curvature distribution diagram; simulating a light propagation path based on the distribution map, obtaining an illumination intensity adjustment coefficient, optimizing an illumination condition through adjustable light source equipment, and collecting an image data set; gaussian filtering is adopted to carry out de-processing on the image data, and then binarization processing is carried out to obtain a subfissure binary image; calculating length and width indexes of the crack according to the hidden crack binary image, determining a hidden crack position set, and aligning the hidden crack position set with the curvature distribution map to generate addition data; and finally, fusing the hidden crack position with the curvature distribution diagram to generate a preliminary report diagram, and generating a final hidden crack identification result through image synthesis. According to the invention, the detection precision of the hidden crack is effectively improved, and the method is especially suitable for detecting the hidden crack defect generated in the laser drilling process of a photovoltaic panel.

The invention discloses an infrared image vertical stripe removing method based on dynamic adjustment of vertical stripe distribution. The method comprises the following steps: (1) a high-dimensional feature vector matrix extraction step, and (2) a vector parameter extraction step: inputting the high-dimensional feature vector matrix and each frame of to-be-processed infrared image into a vertical line removing parameter generation network together, and generating two column vector parameters and two row vector parameters; 3) an improved traditional vertical stripe removing algorithm processing step which comprises the steps of performing Gaussian filtering on the infrared image to be processed in the row direction, calculating vertical stripe noise column by column, judging whether the vertical stripe noise is real vertical stripe noise column by column, obtaining a vertical stripe noise image of the image to be processed, and then performing subtraction to obtain an infrared image after vertical stripe removal; according to the method, a proper loss function and a proper training process are designed for the vertical stripe distribution information extraction network and the vertical stripe removal parameter generation network, so that the model achieves good performance, the distribution condition of vertical stripes can be effectively analyzed, and robust vertical stripe removal parameters are generated.

The invention relates to a spacecraft maneuver detection method based on TLE data and kernel density estimation. The invention relates to the technical field of spacecraft maneuver detection. The method comprises the following steps: acquiring and inputting satellite historical TLE data; gaussian filtering denoising is carried out on the input data; based on the de-noised data, propagation forecasting is carried out through an SGP4 model; constructing a joint residual sequence, and carrying out error distribution modeling and sample scoring; determining a maneuvering threshold value, and judging to obtain abnormal data; and aggregating abnormal data and carrying out maneuvering identification. The method can still realize accurate and stable track maneuver automatic detection under the constraint conditions of unstable TLE data, complex error distribution, no external auxiliary data and the like. The method has good adaptability, robustness and interpretability, and is a track anomaly detection technical scheme which is complete in structure, rigorous in logic and capable of achieving engineering landing.

The invention belongs to the field of optical element detection, and particularly relates to an SD-OCT system for optical element thickness detection and a detection method thereof, and the detection method comprises the steps: carrying out the mask and Gaussian filtering processing of an OCT image of a to-be-detected optical element; carrying out peak point detection on the OCT image subjected to Gaussian filtering by adopting a standard deviation peak searching algorithm, and identifying a light intensity abrupt change position at a film layer interface; carrying out mode analysis on the detected peak point, and determining the number of film layers; performing connected domain analysis on the peak points after mode analysis, filtering out isolated peak points, and performing interpolation processing on missing positions; and performing curve fitting on the peak points after the connected domain analysis, determining the position of each film layer, and calculating the actual thickness of the optical element to be measured. According to the detection method, the steps of mask, Gaussian filtering, standard deviation peak searching, mode analysis, connected domain filtering and the like are fused, artifacts and noise interference are effectively eliminated, and accurate positioning of the film boundary is achieved.