240 results about "Gaussian filter" patented technology

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 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 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 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 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 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 discloses a light field reconstruction brain image brain region boundary extraction method based on convex hull fitting. The method comprises the steps that the p percentile of a brain image is calculated, and image cutting, contrast stretching and Gaussian filtering smooth denoising are carried out; carrying out Canny edge detection on the filtered image to obtain a binary edge image; boundary enhancement and connection are carried out through expansion and closed operation, all connected regions are extracted, and the contour with the maximum area is selected as the contour of the brain region boundary; a datum point is determined based on the contour of the brain region boundary, the polar angle and distance of each point except the datum point are calculated and sorted, and convex hull fitting is carried out through Graham Scan scanning based on the datum point and the sorted point set; converting the convex hull into a closed boundary region, and obtaining a mask image of the brain image after light field reconstruction through a mask function; according to the method, the special boundary form of the light field reconstruction image can be accurately adapted, deep learning and data training are not needed, and the generated mask has high geometric consistency.

The invention discloses a line laser anti-welding noise center point extraction and weld joint point identification method, which is applied to the field of welding quality detection and machine vision measurement. The problems of low extraction precision, poor robustness, difficulty in welding seam point positioning and the like are solved for welding spatter, light reflection and background noise influences. According to the technical scheme, the method comprises the following steps: carrying out Otsu adaptive threshold segmentation, connected domain analysis, morphological opening operation and Gaussian filtering denoising; positioning an interference abnormal line and extracting a preliminary central point by a normal centroid method, and further smoothly extracting a detailed central point in an abnormal region by fitting straight lines of 10 front and back central points to assist judgment; and by calculating a second-order difference value, fitting front and back 50 points at the maximum difference value to identify welding seam points, and optimizing a track. The method can be applied to welding seam positioning and contour detection, the accuracy and stability in a complex noise environment are improved, the welding seam point recognition error is smaller than or equal to 0.2 mm, and the actual requirement is met.

Disclosed in the present invention are a method and apparatus for processing vibration signals of a circuit breaker, a device, and a storage medium. The method comprises: inputting vibration data of a circuit breaker into a modified Laplacian of Gaussian filter, to enhance the transition between background noise and signal features in the vibration data, and output filtered vibration data; and squaring the filtered vibration data, extracting the signal features corresponding to the vibration data, and then identifying an abnormal state of the circuit breaker on the basis of the extracted signal features, wherein the generation of the modified Laplacian of Gaussian filter comprises: separately performing first-order differentiation and second-order differentiation on a Gaussian filter to obtain a corresponding LoG filter; and normalizing the LoG filter, and enabling the sum of taps of the LoG filter to be zero to obtain the modified Laplacian of Gaussian filter. By means of the present invention, filtering of environmental noise and effective distinguishing of signal features in vibration data can be achieved.

A CT full-chain intelligent reconstruction method based on deep learning. Original measurement data is sequentially processed by a trained pixel-wise intelligent correction network, a trained angle-wise intelligent filtering network and a trained back-projection tensor intelligent reconstruction network to finally obtain a final reconstructed CT image. In the present invention, firstly, a pixel-wise intelligent correction network can perform pixel-wise adaptive Gaussian filtering on low-dose original measurement data by learning the variance of each pixel point. Moreover, an angle-wise intelligent filtering network can perform angle-wise adaptive filtering on sinogram data by learning a filtering kernel of each projection angle. Finally, by learning the mapping of a back-projection tensor to a target image, a back-projection tensor intelligent reconstruction network can ensure image quality while reducing the scanning dose.

The invention discloses an image real-time rendering processing method in a digital twinborn scene, which relates to the technical field of real-time rendering and comprises the following steps: a multi-source data preprocessing step: noise reduction is carried out on an RGB image of the digital twinborn scene by adopting self-adaptive Gaussian filtering which dynamically adjusts the size of a filtering kernel according to the pixel gray level difference, and the noise of the RGB image is reduced; the depth image is processed through bilateral filtering with the coefficient of 0.8-1.2, and lightweight data processing is carried out, specifically, a unified world coordinate system is established, and three-dimensional model vertex data and image pixel coordinates are registered, and the method has the advantages that through differential noise reduction, unified coordinate registration and lightweight processing, data distortion and offset are reduced, and the precision of the image is improved. Fusion efficiency is improved, rendering delay is solved, and scene change and hardware difference are adapted by collecting hardware and performance parameters in real time, establishing an intelligent adjustment mechanism and matching scene switching preloading and transition frames.

The invention relates to the technical field of computer vision and video processing, and provides a residual frame video processing convolution method based on dynamic gating, which comprises the following steps of: carrying out gray conversion and Gaussian filtering processing on a current frame image and a previous frame image; calculating an inter-frame difference image and carrying out binarization processing to obtain a motion area mask; performing morphological expansion operation on the mask image; extracting the contour of the motion area and calculating a minimum bounding rectangle to obtain a bounding box; carrying out merging processing on the overlapped bounding boxes; updating the synthesized background frame based on the combined bounding box, and copying the pixels of the motion area of the current frame to the corresponding position of the background frame; and outputting the optimized bounding box set and the updated synthesized background frame. According to the method, the calculation efficiency and the resource utilization rate of video processing are improved, and meanwhile, the timing sequence continuity and the space consistency of a processing result are ensured through a strategy of keeping the static region unchanged and only updating the motion region.

The embodiment of the invention provides a near-field MIMO radar data processing method based on multistage background suppression and target enhancement, and is applied to the technical field of radar signal processing. The method comprises the following steps: acquiring a real-time background template and a real-time image frame of a target scene; calculating to obtain differential image data corresponding to the real-time image frame based on the real-time background template and the real-time image frame; performing Gaussian filtering processing on the difference image data to obtain Gaussian filtering image data; performing morphological processing on the Gaussian filtering image data to obtain morphological image data; performing target enhancement processing on the morphological image data to obtain target enhanced image data corresponding to the real-time image frame; and outputting the target enhanced image data. The technical effect of improving the radar imaging precision is achieved.

The invention discloses a core optical image equalization processing method based on a domain adaptive residual error full connection network, comprising the following steps: S1, estimating illumination components based on a Retinex theory and dynamic Gaussian filtering, realizing brightness domain adaptive division according to the difference of the illumination components, and recording boundary position coordinates of each partition; s2, cutting the original color digital rock core image according to the boundary coordinates of each partition recorded in the step S1, and storing the cut original color digital rock core image as an independent image file; s3, constructing a residual error full-connection network combining a transfer learning strategy and a domain adaptation algorithm, wherein a training target is to minimize a domain adaptation loss function to obtain an optimal prediction model; s4, using the optimal prediction model to correct an image edge region and outputting a brightness-equalized core image; according to the method, the brightness equalization effect can be remarkably improved while image details are kept, and the accuracy and generalization ability of the lithology recognition model can be greatly improved.

The invention discloses a signal denoising method and device based on MEEMD and northern eagle optimization, and belongs to the technical field of signal processing and gas detection. The method comprises the following steps: acquiring a second harmonic signal in photoacoustic spectrometry gas detection; performing MEEMD decomposition on the signal to obtain a plurality of intrinsic mode function IMF components and residual errors; calculating the energy ratio of each IMF component and screening effective IMF components; reconstructing the effective IMF component and the residual error to obtain a preliminary de-noised signal; carrying out self-adaptive smoothing processing on the preliminarily denoised signal; gaussian filtering is carried out on the smoothed signal; and optimizing an energy threshold value, a self-adaptive smooth window parameter and a Gaussian filtering parameter by using a northern eagle optimization algorithm to obtain an optimal parameter combination. According to the method, MEEMD modal decomposition, Gaussian filtering and NGO parameter optimization are fused, accurate denoising and feature retention of gas concentration signals are achieved, and the precision and reliability of photoacoustic spectrum gas detection are remarkably improved.

The invention relates to the technical field of metal plate detection, and discloses a metal plate bending defect detection method and system based on industrial vision, and the method comprises the steps: obtaining original image data, and obtaining a denoised image through Gaussian filtering; a technological parameter set is obtained, and a technological parameter deviation index is obtained through normalization and mean value deviation calculation; extracting edge features of the denoised image by using a convolutional neural network, segmenting according to a preset fixed threshold to obtain a defect width region, and counting and filtering to obtain an initial defect width value distribution set; associating the parameter deviation index with the initial distribution set to calculate an adjustment factor, weighting to obtain an accurate defect width value distribution set, mapping the accurate defect width value distribution set with the process parameter set, and fusing the de-noised image and the parameter deviation to obtain a defect tracing path; and extracting textural features according to the defect tracing path, judging the linkage influence of crack propagation and a mold gap, and determining an optimized process parameter scheme in combination with a preset database. The method can improve the defect detection precision and the production process adjustment efficiency.

The invention discloses an AI linkage data system based on Internet of Things multi-source data fusion, and relates to the technical field of image recognition, and the system comprises a collection module which is used for collecting a real-time image of a fire, and selecting a suspected region with the brightness higher than a brightness threshold value in the real-time image; the preprocessing module is used for performing Gaussian filtering preprocessing on the suspected area; the method comprises the following steps: denoising a suspected region by dynamically adjusting Gaussian filtering parameters, calculating a brightness change rate of pixels on the inner and outer sides of an edge to obtain a gradient band width, dividing suspected region sub-blocks, extracting sub-block texture feature vectors by adopting a local binary pattern algorithm, and calculating a region overall texture entropy value by combining a region texture similarity matrix to obtain a region texture similarity matrix; the difference between the strong reflection light spots and real flames can be accurately recognized, the AI chip is prevented from misjudging the strong reflection light spots as newly added fire behaviors, then the linkage control module is prevented from mistakenly starting a spraying system in a non-fire behavior area, and damage of a large number of materials due to water stains caused by mistaken spraying is reduced.

This invention relates to the field of intelligent molding technology, specifically to an intelligent adjustable medical cotton ball molding method and system, comprising the following steps: collecting pressure data through a distributed pressure sensor array and generating a pressure feature vector through Gaussian filtering for noise reduction; analyzing the pressure change trend based on the feature vector and outputting a gradient adjustment command; controlling a valve to adjust the air pressure to obtain compensation parameters; driving a servo motor to adjust the mold gap and collecting offset data to generate a feedback signal; and combining the feedback and adjustment command inputs to a neural network to evaluate the molding result. In this invention, data is collected through a distributed sensor array, multi-dimensional noise reduction enhances feature extraction, dynamic functions analyze pressure trends, offset adjustment parameters are identified, collaborative control achieves closed-loop compensation, displacement sensors provide feedback on gap changes, parameter modeling optimizes accuracy and consistency, convolutional networks extract temporal features, and fusion analysis constructs an adaptive system to achieve intelligent prediction and optimization of molding quality.

The application discloses a kind of based on edge identification no target cable force identification method, the method includes: cable video acquisition and pre-processing: acquisition cable video, video frame is carried out image gray conversion and is adapted to the multi-scale Gaussian filtering operation of filter scale according to noise characteristics;Canny algorithm cable edge identification: using Canny edge detection algorithm to the video frame after pre-processing is carried out cable edge identification;Cable feature point screening and KLT optical flow method identification: from edge identification result screening cable feature point, and using KLT optical flow method in continuous video frame between tracking feature point dynamic displacement;Fundamental frequency identification and cable force calculation: displacement data is applied Fourier transform to identify cable fundamental frequency, and adopts XGBOOST regression model based on fundamental frequency and relevant parameter calculation cable tension.The application realizes the precise, efficient and non-contact monitoring of bridge cable stress state.

The present application relates to a kind of solar filament fine structure enhancement method based on line Gauss filter, belong to image enhancement field.To overcome the current H-alpha image in solar filament fiber because of low contrast, leading to artificial marking difficult, the problem that the fiber attribute information is not accurate enough is extracted, the present application proposes a kind of image enhancement method with line Gauss convolution as core.Firstly, H-alpha image is normalized to preliminary stretch image contrast, while suppressing noise using Laplace-Gauss operator to enhance edge, then using line Gauss filter to enhance the contrast of solar filament fiber.In post-processing stage, using the adaptive histogram equalization of restriction contrast and top hat, bottom hat transformation method to improve the uneven problem of line Gauss filter enhancement effect.Finally, line Gauss filter is used again to further improve the contrast of solar filament fiber.After the enhancement of the method, the filamentary structure of solar filament fiber is clear, which makes it possible to objectively and accurately measure the filamentary structure attribute information of solar filament.

This invention relates to the field of smart fisheries technology, specifically to a visual recognition-based automatic fish feeding control method, which includes the following steps: A pan-tilt-zoom camera, equipped with an infrared / enhanced light source, is installed at a suitable location to cover the fish feeding area, continuously acquiring water surface images and videos to obtain fish feeding activity data; noise reduction is performed using mean, median, and Gaussian filters; a target detection model is selected; the fish feeding status is determined to initiate feeding operations; the decision results drive the operation of the feeding equipment; alarm thresholds are set; operation logs are recorded; and a strategy report is generated using a big data algorithm, resulting in feeding operations, monitoring footage, and the strategy report. This invention solves the problem in traditional aquaculture where the lack of effective monitoring methods makes it difficult to accurately obtain fish feeding data, hindering the achievement of intelligent and efficient aquaculture.

This invention relates to an image denoising method, system, and readable storage medium. The method includes: performing Gaussian filtering and downsampling on a current frame image to obtain a first downsampled image; performing Gaussian pyramid decomposition to obtain a current frame image group; upsampling to obtain an upsampled image, and subtracting it from the current frame image to obtain high-frequency information; performing motion estimation and texture estimation at different scales on the current frame image group and a reference frame image group, and correcting them with guided filtering; performing spatiotemporal filtering on the first downsampled image and the reference frame image based on motion estimation weights; upsampling the denoised downsampled image to obtain a denoised upsampled image; and weighted fusing the denoised upsampled image and high-frequency information according to texture estimation weights to obtain a final image; downsampling the final image and performing Gaussian pyramid decomposition to obtain a reference frame image group for denoising the next frame image. This invention can preserve more texture details while increasing the image signal-to-noise ratio.

The application discloses an environment monitoring method and device, which is used for realizing accurate counting of river floating objects in a river monitoring image, so that a more accurate and real-time monitoring result of the river environment quality is obtained. The environment monitoring method provided by the application comprises the following steps: determining a river monitoring image; performing Gaussian filtering and smoothing processing on the river monitoring image, and performing down-sampling on the image obtained through the Gaussian filtering and smoothing processing for a preset number of times; constructing a Gaussian pyramid model by using the image obtained through the Gaussian filtering and smoothing processing and the image obtained through each down-sampling; the Gaussian pyramid model comprises multiple layers of images, wherein the bottom layer of image is the image obtained through the Gaussian filtering and smoothing processing, and each layer of image other than the bottom layer is the image obtained through one down-sampling; determining a region of interest of the river monitoring image by using each layer of image of the Gaussian pyramid model; counting the river floating objects in the region of interest; and determining the river environment quality based on the counting result.

A wireless communication system provides a baseband signal processing circuit configured to sample a baseband signal at twice a baseband frequency to produce an upsampled signal that provides a sequence of samples of the baseband signal at twice the baseband frequency; and combine each sample within the upsampled signal with a preceding sample within the upsampled signal to produce a modified baseband signal that provides a sequence of modified symbols at twice the baseband frequency; a modulator circuit coupled to modulate frequency of a carrier signal based upon symbol values of symbols within the modified baseband signal to produce a frequency modulated carrier signal; and an antenna coupled to transmit the modulated carrier signal.

The present application relates to the technical field of defect detection, and particularly relates to a kind of electric energy meter appearance defect detection method based on machine vision, the method obtains the appearance image of electric energy meter, obtains corresponding gray image;For any pixel point in gray image, at least two local windows of different sizes are constructed in gray image with any pixel point as center, according to the distance between each pixel point in each local window and any pixel point and local gray correlation, the possibility index that any pixel point is disturbed by illumination is obtained;According to the possibility index that each pixel point in gray image is disturbed by illumination, the adaptive Gaussian scale of each pixel point when carrying out Gaussian filtering to gray image is obtained, to extract the reflection component of gray image;Gradient histogram of reflection component is constructed, entropy value of gradient histogram is calculated, defect entropy threshold is obtained, appearance defect detection is carried out to electric energy meter, and the precision of defect detection is improved.