633 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 relates to the technical field of image recognition, in particular to an intelligent electric energy meter image collection method and system based on multi-source data, and the method comprises the following steps: collecting a multi-angle reflection contour image of an electric energy meter through a preset incident angle light source, and calculating a difference value point by point based on a standard reference contour point coordinate; and after edge sections are divided, the maximum value, the minimum value and the mean value are processed by adopting a Gaussian filtering algorithm, and a multi-angle difference statistical result is generated. According to the invention, through multi-angle reflection contour image acquisition, difference calculation and edge division, Gaussian filtering noise reduction, ultralimit edge segment extraction, angle focal length compensation through step length adjustment, edge extraction through a Canny edge detection algorithm and contrast change rate calculation, dynamic path instruction generation, high-frequency feature point density extraction through SIFT, and direction angle adjustment scanning sequence analysis, the method can be used for realizing multi-angle detection of the image. Calculating a standard deviation and a gradient variance to generate an abnormal graph, performing local focusing to compensate an abnormal region, performing gray scale consistency splicing replacement, and performing multi-source response optimization closed-loop correction.

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 relates to the technical field of image analysis, in particular to a QFP chip image pyramid positioning system which comprises a pyramid construction module, a pin initial positioning module, a pin accurate positioning module and an interference area elimination module. According to the method, an original chip image is processed through step-by-step down-sampling and Gaussian filtering, the image processing speed is effectively improved when a multi-layer image pyramid is constructed, a pin connected domain is clearly identified by applying binarization processing and a pin neighbor connection strategy, the pin area identification capability of the system is enhanced, and the identification accuracy of the system is improved. Region recalibration is carried out by using an up-sampling image of an original image data size, accurate pin positioning can be executed while high resolution is maintained, the probability of wrong positioning is remarkably reduced by accurately extracting pin contour information and effectively eliminating non-target connected domains, the chip assembling quality is improved, the chip assembling speed is increased, and the chip assembling efficiency is improved. And remarkable benefits are brought to a high-speed electronic assembly line.

The invention belongs to the technical field of face recognition, and particularly relates to a face recognition algorithm adaptive to illumination variation, which uses Gaussian filtering to reduce noise caused by illumination variation, convert a color image into a grey-scale image, reduce calculation complexity, perform adaptive histogram equalization, estimate global illumination conditions in the image and improve the face recognition accuracy. Calculating the main direction and intensity of illumination in the image, predicting the current illumination condition, and enabling the image brightness to adapt to different environment illumination; identifying a face region in the image, using a deep learning method to position face related points, using an LBP to extract local features insensitive to illumination variation, and using a deep convolutional neural network to extract global features; introducing a multi-illumination data set; the Euclidean distance is used for comparing the extracted feature vectors, the similarity between the extracted feature vectors and known faces in a database is recognized, according to the similarity score, threshold judgment is adopted for AQW to obtain a final recognition decision, and the method has the effect of being capable of accurately adapting to facial features under different illumination conditions in real time.

The invention relates to the technical field of image data processing, in particular to a shell defect detection method and system based on machine vision, and the method comprises the steps: collecting a to-be-detected shell image, and carrying out the smoothing of a to-be-detected shell image through an improved Gaussian filtering algorithm, and obtaining a corrected image; carrying out edge detection on the corrected image, and if the number of detected edge contours is greater than a set threshold value, judging that flaws exist; wherein in the improved Gaussian filtering algorithm, the Gaussian weight is positively correlated with the noise calibration degree of the pixel point, and is inversely correlated with the mean value of the pixel gradient amplitudes in the set window; the noise calibration degree is in positive correlation with the pixel noise possibility degree and the gray variance in the window and is in inverse correlation with the noise possibility degree in the window, and the noise possibility degree represents the defect degree. According to the method, the problems that details are lost and noise and defects are inaccurately distinguished due to excessive smoothness when an existing Gaussian filtering algorithm is used for shell detection are solved.

The invention discloses a sea ice target detection method based on multi-dimensional feature extraction, and the method specifically comprises the following steps: S1, data preprocessing: carrying out the denoising of an original sea ice image through a Gaussian filter, carrying out the multi-scale analysis of the sea ice image through an image pyramid, and obtaining sea ice images under different scales; s2, local feature extraction: extracting texture features of each local region by using a gray-level co-occurrence matrix; s3, performing enhanced texture feature processing on the extracted texture features through a directional filter and an enhancement algorithm to obtain final texture feature data; and S4, performing feature fusion on the obtained multi-dimensional features and the texture features to form a comprehensive feature image, and obtaining a classification result of the sea ice target. According to the sea ice target detection method, through multi-scale analysis, image representation is obtained under different scales by using an image pyramid transformation method, changes and expressions of texture features under different scales are captured, and the stability of sea ice target detection is improved.

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 broadband impedance spectroscopy cable fault positioning method based on end function compensation, and the method comprises the steps: determining a voltage and current partial differential equation set of a healthy coaxial cable according to an infinitesimal equivalent model of the healthy coaxial cable, and calculating a general solution of the voltage and current partial differential equation set; calculating a reflection coefficient of a load end of the healthy coaxial cable according to the general solution, and calculating an impedance spectrum of the healthy coaxial cable about frequency according to the reflection coefficient and the general solution; measuring the impedance spectrum of the tested cable about the frequency, and comparing the impedance spectrum with the impedance spectrum of the healthy coaxial cable to determine whether the tested cable has a fault or not; constructing an orthogonal integral function according to the phase spectrum, introducing Gaussian filtering to eliminate local interference caused by a large fault, and extracting an extreme value and an extreme value of the function to form a compensation function; a fault diagnosis function based on the impedance phase spectrum is established, the local fault position and the local soft fault type of the detected cable are determined, accurate positioning of different fault positions is achieved, and meanwhile the detection and positioning precision is greatly improved compared with a traditional method.

The invention discloses a data processing method and system based on image acquisition equipment, and relates to the technical field of computer vision, and the method comprises the steps: constructing CNN-VAE to generate a low-light image of a multi-modal image, employing an inverse attention module and a GAN model of a double-discriminator structure, and combining parallel expansion and expansion convolution to expand a training data set; images in the training data set are enhanced through double-branch U-Net, image details are enhanced through local variance adaptive Gaussian filtering, pixel gradients are classified through a support vector machine, and edge detection is carried out; resNet50 is adopted to extract features, and a candidate frame is accurately positioned in combination with RPN and non-maximum suppression. Through CNN-VAE and an anti-attention GAN model, training data is expanded, double-branch U-Net and local variance adaptive Gaussian filtering are utilized to enhance image details, edge detection is optimized through a support vector machine, ResNet50 feature extraction is adopted, an RPN network and non-maximum suppression are combined to position an object candidate frame, and high-precision object detection and recognition are realized. And the precision and robustness of image processing are obviously improved.

The invention relates to the technical field of sparse radar depth completion, in particular to a sparse radar depth completion method based on Boolean mask constraint. The method comprises the following steps: generating a plurality of Boolean masks, processing the Boolean masks, and superposing the processed areas into a depth map; performing depth reversal operation to obtain a reversed depth value; performing multi-scale convolution kernel expansion operation to obtain an expanded depth map; performing mask-constrained hole closing operation to obtain a depth map after hole closing; performing depth value upward extension operation to obtain an extended depth map; performing mask-constrained hole filling operation to obtain a depth map after hole filling; performing median filtering and Gaussian filtering operation on the depth map after hole filling to obtain a filtered depth map; and performing depth map reversal operation on the filtered depth map to obtain a final depth map. The method can effectively protect the deep fracture area, and improves the performance of the depth completion algorithm.

The invention discloses a multi-source ship target identification method based on two-stage collaborative fusion, and relates to the technical field of signal processing, and the method comprises the steps: firstly collecting target sea area data, including original optical image and radar image data, by means of an optical camera and a radar sensor on an aircraft, and then carrying out the preprocessing of the collected data through Gaussian filtering; performing sea-land segmentation by using a deep learning algorithm, constructing a loss function optimization network parameter to obtain a segmentation mask, and removing a land background area according to a mask result to obtain processed optical and radar images; an improved SURF feature and graph cut algorithm combined registration method is adopted, optimal transformation parameters are obtained by screening feature point pairs and solving the minimum value of an energy function, and image registration is achieved; for the registered images, fusing the images based on an adaptive fusion rule; and finally, extracting corresponding features through manual feature extraction and feature extraction based on deep learning, and fusing the features to obtain a final target recognition result.

A data-centric system for analyzing agricultural crops, consisting of: a data acquisition module configured to capture images of agricultural fields using cameras, unmanned aerial vehicles (UAVs) or sensors, with the sensors collecting data on soil moisture, temperature, light, humidity and pH; a data preprocessing module configured to: resize the acquired images to a standardized dimension suitable for input to a deep learning model; apply noise reduction using a Gaussian filter; improve image contrast through histogram equalization; and perform image magnification through rotation, reflection, and scaling transformations; a feature engineering module configured to extract the following features: color features, which include color histograms, mean, and standard deviation of color channels; texture features using Gray-Level Co-occurrence Matrix (GLCM) properties, which include contrast, dissimilarity, homogeneity, energy, angular moment (ASM), and correlation; shape features, which include contour area, perimeter, aspect ratio, and roundness; and other features, which include the green pixel ratio and edge density; a classification module configured to: implement deep learning-based classification models selected from the group consisting of Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), ResNet18, Random Forest (RF), SegNet, VGGNet, Naive Bayes (NBG), Decision Tree (DT), K-Nearest Neighbors (KNN), and DeepLab; detecting and classifying weed species in the images of agricultural fields; and diagnosing plant diseases based on the features extracted from the images of agricultural fields; an output module comprises a user interface configured to display the classification and recognition results; and a recommendation module configured to suggest treatment solutions for diagnosed plant diseases through the output module's user interface.

The invention relates to the technical field of intelligent forming, in particular to an intelligent adjusting type medical cotton ball forming method and system.The intelligent adjusting type medical cotton ball forming method comprises the following steps that pressure data are collected through a distributed pressure sensor array, pressure feature vectors are generated through Gaussian filtering denoising, a pressure change trend is analyzed based on the feature vectors, and a gradient adjusting instruction is output; and controlling the valve to adjust air pressure to obtain compensation parameters, driving the servo motor to adjust a mold gap, collecting offset data to generate a feedback signal, and inputting a neural network to evaluate a forming result in combination with feedback and adjustment instructions. According to the method, a distributed sensor array collects data, multi-dimensional denoising improves feature extraction, a dynamic function analyzes a pressure trend, an offset adjustment parameter is identified, closed-loop compensation is realized through cooperative control, a displacement sensor feeds back gap change, parameter modeling optimizes precision consistency, a convolutional network extracts time sequence features, and a self-adaptive system is constructed through fusion analysis. And intelligent prediction and optimization of the forming quality are realized.

The present invention relates to the technical field of electrical signal processing, and specifically relates to a method and system for monitoring the operating state of a driverless vehicle. Residual amplitudes, which do not have Gaussianity, are screened out by means of iteration, a vibration element is designed on the basis of the largest residual amplitude and the smallest residual amplitude among the screened-out residual amplitudes, and a second original signal is obtained by performing convolution on a first original signal. Then, segmentation and matching are performed on an upper envelope and a lower envelope of the second original signal, and the symmetry and the maximum amplitude difference of a matching signal segment are calculated; and then an estimated intensity of residual noise is obtained on the basis of the correlation between the symmetry and the maximum amplitude difference, so as to generate Gaussian filtering having the same intensity, and smoothing is performed on component signals obtained after the second original signal has been subjected to LMD, so as to filter out deep noise. In the present invention, a monitoring data pre-processing system is optimized by means of an adaptive vibration element and an adaptive Gaussian filtering kernel, thereby effectively improving the precision of monitoring data.

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 discloses a system and method for measuring the opening and closing state of an electromagnetic valve, and relates to the technical field of electromagnetic valves.The method comprises the steps that after an electromagnetic valve control signal is triggered, dynamic inductance L at each moment t is obtained through real-time backstepping by means of a voltage and current sampling module integrated in a driving loop and an inductance analysis module in a local microcontroller MCU; gaussian filtering processing and derivative calculation are carried out on the dynamic inductance L in the edge calculation module, first-order derivative and second-order derivative characteristics are extracted, and an inductance mutation factor index Cimp is obtained through calculation. According to the method, clamping stagnation, rebound and discontinuous abnormal states occurring in the opening and closing process of the electromagnetic valve are comprehensively reflected, a sudden change threshold value Cthr is set and compared, and a set of dynamic opening and closing behavior diagnosis mechanism with high resolution and high adaptability is constructed. According to the method, high-frequency dynamic response extraction and abnormal trend quantification can be completed within 100 ms after the control signal is triggered, and the method has higher early warning capability and non-intrusive compatibility.

The invention relates to the technical field of image processing, and discloses a morphology-based image texture feature extraction method, which comprises the following steps: an image preprocessing step: carrying out graying processing on an original image, and carrying out smoothing processing on a grayscale image by using a Gaussian filter; a morphological operation step: selecting structural elements suitable for image texture characteristics, performing expansion and erosion operation on the smoothed image, performing opening operation on the expanded image, and performing closing operation on the eroded image; and an adaptive feature selection step: carrying out local binary pattern feature extraction on the image after morphological processing, adaptively determining a threshold according to statistical distribution of LBP features, and segmenting the image into a foreground and a background. The image texture feature extraction method based on morphology aims at solving the problems that feature extraction is inaccurate, noise interference is likely to happen, and feature combination is complex in an existing image texture feature extraction method.

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 a pressure damage classification system based on image processing, which belongs to the technical field of image processing and comprises a pressure damage image acquisition module, a pressure damage image preprocessing module, a pressure damage classification model construction module and a pressure damage classification module. According to the method, a comprehensive noise characteristic value is obtained based on a pixel variance, an LBP value variance and a distance weight, an adaptive filtering weight is calculated, bilateral filtering and Gaussian filtering are fused for denoising, a detail enhancement factor is generated in combination with a local entropy value and a Sobel gradient amplitude, detail enhancement is carried out, the local contrast is enhanced, and the image quality is remarkably improved; according to the method, multi-scale features are extracted and fused, a space-channel attention mechanism is introduced to obtain a global weight, global enhanced fusion features and local enhanced fusion features are obtained, and comprehensive features are generated through fusion, so that the capability of identifying stress damage in different stages is remarkably improved.

The invention relates to the technical field of image enhancement, in particular to a surface process defect detection method and system for copper foil production, and the method comprises the steps: collecting a copper foil image in a production process; obtaining each image block in the copper foil image; performing edge detection on the copper foil image to obtain each edge line in the copper foil image; determining a texture feature coefficient of each image block, constructing an illumination contrast coefficient of each image block, correcting a standard deviation parameter in a Gaussian filter, and performing image enhancement on each image block by using the Gaussian filter after parameter correction and a local Retinex algorithm; and carrying out surface defect detection on the copper foil through the enhanced copper foil image. Therefore, the precision of copper foil surface process defect detection is improved.

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 belongs to the technical field of image processing, and particularly relates to a bridge pier intelligent inspection method, which comprises the following steps of: performing Gaussian filtering on an inspection image through Gaussian filters with different scales, and subtracting an original inspection image from obtained background images with different scales to obtain intermediate images with different scales; calculating the enhancement degree of the gradient features of the pixel points in the intermediate images under different scales compared with the gradient features of the pixel points in the original inspection image; calculating the retention degree of the multiple LBP features of the pixel points in the intermediate image under different scales compared with the original inspection image; and according to the enhancement degree and the retention degree, calculating the weights of the gray values of the pixel points in the intermediate image under different scales, and carrying out weighted summation on the gray values of the pixel points in the intermediate image under different scales so as to obtain a uniform light image. According to the invention, the finally obtained uniform light image achieves better balance in detail and texture aspects.

The invention provides a universal method for automatically identifying woven fabric density based on deep learning, and relates to the field of artificial intelligence and image analysis and processing. The deep learning-based universal method for automatically identifying the density of the woven fabric comprises the following steps of: a, acquiring a high-resolution color woven fabric image by an image preprocessing module, and carrying out graying processing on the high-resolution color woven fabric image; the method comprises the following steps: automatically identifying the inclination angle of the yarn and carrying out coordinate point transformation to obtain an inclination-corrected grayscale image, obtaining a grayscale projection discrete curve in the longitude and latitude directions of the image and carrying out smoothing processing by adopting Gaussian filtering to obtain a minimum value point of discrete data; the universal method for automatically identifying the woven fabric density based on deep learning has the advantages that 1, the automation degree is high: the end-to-end density automatic identification is realized based on the deep learning technology, the manual intervention is reduced, and the detection efficiency and accuracy are improved; compared with a traditional method, the steps of manual proofreading and material classification are avoided, and the whole recognition process is more efficient.

The invention discloses a scene understanding and intelligent decision-making method for a planet probe vehicle in a complex unknown environment, and relates to the technical field of deep space exploration and intelligent vehicles, and the method comprises the steps: obtaining multi-dimensional environment information; constructing a slump function based on the geometric morphology, motion characteristics and material attributes of the obstacle, and dynamically quantifying the risk of the obstacle; smoothing the potential field function by adopting Gaussian filtering, and dynamically adjusting the weight of a global potential field and the weight of a local potential field; in combination with an improved clustering algorithm, fuzzy regions of obstacles capable of being crossed and obstacles not capable of being crossed are accurately divided; screening passable areas by constructing a credibility mean square value function; carrying out multi-objective optimization by adopting a genetic algorithm, and generating an optimal path matched with the environment in cooperation with an offline data set and online reasoning; and inputting the optimal path into a bottom layer control system of the probe vehicle, controlling execution of a motor driving and steering mechanism, monitoring the state of the probe vehicle in real time, and triggering dynamic re-planning. And the autonomous obstacle avoidance capability and the path planning efficiency of the probe vehicle in a complex environment 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.