300 results about "Denoising algorithm" patented technology

A detection method for infrared thermal image damage area of coal rock includes: step 1: collecting and recording the infrared thermal image of coal rock in the process of loading destruction; step 2: processing a gray-scale transformation on the collected infrared thermal image of coal rock; step 3: denoising an infrared thermal image of coal rock after the gray-scale transformation by the dense residual image denoising algorithm of autocorrelation network; step 4: conducting an area segmentation on the infrared thermal image of coal rock after denoising to extract eigenvalues by using a coal-rock infrared thermal image damage area segmentation algorithm of improved encoder-decoder network; step 5: observing the damage area of coal rock. The detection method improves the denoising effect of the infrared thermal image of coal rock, strengthens the extraction of the characteristics of the damage area, and improves the accuracy of the segmentation.

The invention belongs to the technical field of computer vision, and discloses a wire harness crimping defect detection system and method based on machine vision, and the system comprises a multispectral light source control module which is used for employing an annular LED light source and coaxial light mixed illumination and adapting to different wire harness surface materials by dynamically adjusting the angle and intensity of the light source; the multi-dimensional perception acquisition module is used for acquiring wire harness image data and crimping geometric data in real time based on the dynamically adjusted light source angle and intensity; the visual enhancement fusion module is used for de-noising the wire harness image data by using a non-local mean de-noising algorithm and dynamically adjusting and controlling a similarity measurement range through a smooth factor; superposing a multi-scale Retinex algorithm, and enhancing the texture contrast of the wire harness image data through the local contrast; the wire harness image data and the crimping geometric data are fused, and a crimping part multi-mode characteristic graph is constructed; the accuracy of defect detection is facilitated, and potential problems can be found and solved in time.

A cooling capacity prediction method and system for heating, ventilation and air conditioning and a related device. The method comprises: by means of a denoising algorithm, decomposing an original signal into multiple intrinsic modes and a residual, the multiple intrinsic modes constituting an intrinsic mode function; using the Pearson correlation coefficient to classify the intrinsic modes into a first intrinsic mode dominated by an effective signal and a second intrinsic mode dominated by noise; decomposing the second intrinsic mode by means of wavelet soft thresholding denoising to obtain a third intrinsic mode; reconstructing the first intrinsic mode and the third intrinsic mode to finally obtain denoising data; taking the denoising data as an input of a preset neural network to finally obtain a cooling capacity prediction model, and using the cooling capacity prediction model to predict the cooling capacity of heating, ventilation and air conditioning to obtain a prediction result of the cooling capacity of the heating, ventilation and air conditioning. Compared with the prior art, the present invention solves the problems of nonlinearity, coupling, and long-term dependency in a cooling capacity prediction process, and increases the speed and accuracy of the cooling capacity prediction model.

The invention discloses a multi-scale adaptive time sequence InSAR (Interferometric Synthetic Aperture Radar) earth surface deformation extraction method, and relates to the field of remote sensing signal processing. The method comprises the following steps of: constructing an unwrapped time sequence InSAR phase simulation data set, performing pixel-by-pixel time sequence empirical mode decomposition on a time domain, obtaining a time sequence intrinsic mode function at the same pixel position, and constructing a FastICA wavelet enhancement denoising algorithm on a space domain: performing two-dimensional multi-stage wavelet decomposition on the intrinsic mode function of each dimension by taking a power spectrum entropy as a cut-off condition, the method comprises the following steps of: acquiring a wavelet approximation coefficient of each level and a detail coefficient, performing noise reduction and dimension reduction processing and reconstruction on the wavelet approximation coefficient and the detail coefficient of each level, and finally performing wavelet reconstruction on the updated wavelet coefficient to obtain a processed time sequence InSAR phase simulation data set. The method provided by the invention is based on data self-driving, self-adaptive decomposition is carried out on phase signals from two scales of time and space, earth surface deformation related signals can be effectively captured, other phase signals can be separated and explained, and a scientific theoretical basis is provided for high-precision earth surface deformation monitoring.

The invention relates to an electronic tag production quality control method in the technical field of information, and the method comprises the steps: carrying out the noise reduction of an image through a multi-scale denoising algorithm in a second image for vibration interference and dust interference, and obtaining a third image; the online detection system judges the printing quality of the electronic tag according to the OCR recognition result, and if abnormality is detected, an abnormal signal is generated and transmitted to the process control system; the process control system receives the abnormal signal, adjusts operation parameters of printing, fitting and cutting processes, ensures smooth process connection, and avoids unbalance of the production rhythm; comparing the adjusted process parameters with a preset product consistency standard, and if the parameter deviation exceeds a preset range, starting an automatic calibration module to ensure the product consistency; the operation state of the whole production process is monitored in real time through a system monitoring module, if the stability of the system is reduced, an early warning mechanism is triggered, and a maintenance signal is generated and transmitted to a maintenance system.

The invention relates to the field of phytoplankton image edge detection, in particular to a marine phytoplankton image edge detection method based on a non-local mean denoising mode. The method comprises the following steps: putting a marine phytoplankton image file needing to be detected into a folder of a program item, automatically reading a code into an image in the folder by a program, and loading a to-be-processed image; edge details related to the marine phytoplankton image are reserved based on a non-local mean denoising algorithm; aiming at the image data after the edge details are reserved; carrying out edge extraction on the image based on a Canny edge detection algorithm; and outputting a marine phytoplankton image with edge details reserved. Through combination of non-local mean denoising and Canny edge detection, noise can be more effectively removed, image details and structure information can be kept, and the influence of noise on edge detection is reduced, so that clearer and more accurate edges are extracted; and in an image with relatively large noise, the edge extraction precision and the image quality can be remarkably improved.

The invention discloses an improved filtering and local direction adaptive rust recognition method for a hull surface rust image, which comprises the following steps of: 1, performing gray processing on the hull surface rust image to obtain a gray image; 2, performing global denoising on the image after gray scale by adopting a wavelet transform and total variation fusion denoising algorithm to obtain a globally denoised image; 3, performing local denoising on the globally denoised image by adopting a self-adaptive Gaussian kernel denoising algorithm to obtain a final denoised image; 4, carrying out the edge extraction of the final denoised image through a direction self-adaptive edge detection algorithm, and obtaining a rust image; according to the method, the improved wavelet transform and total variation fusion denoising algorithm is combined, efficient noise removal is achieved, the size and the standard deviation of the filtering kernel are adjusted in a self-adaptive mode, noise of a local area is effectively processed, detail loss is avoided, and the image quality is improved.

The invention discloses an improved filtering and adaptive edge detection steel plate gap identification method for a hull surface image, and the method comprises the steps: 1, carrying out the denoising of a hull surface image containing a steel plate gap through employing a local weighted Gaussian blur and dynamic bilateral filtering fusion algorithm, and obtaining a denoised hull surface image; 2, carrying out the edge detection of a local region of the denoised hull surface image through employing a self-adaptive dynamic threshold Canny edge detection method, and obtaining a binary image; and step 3, carrying out edge extraction on the binary image to obtain a steel plate gap image. According to the method, through a local weighted Gaussian blur and dynamic bilateral filtering fusion denoising algorithm, the noise in the image is efficiently removed in a proportional fusion mode, meanwhile, through self-adaptive adjustment of filtering parameters, the noise influence of a local area is improved in a targeted mode, loss of detail information is avoided, and the image quality and detail presentation are remarkably improved.

The invention discloses a partial discharge on-line monitoring method of a switch cabinet and a storage medium, and relates to the technical field of power equipment monitoring. According to the method, multi-dimensional state parameters such as vibration amplitude and pulse density are obtained in real time by constructing a local, edge and cloud three-level processing architecture, and self-adaptive distribution of calculation tasks is achieved based on a dynamic unloading scoring formula. The local nodes execute time domain feature extraction, the edge nodes perform frequency domain enhancement and confidence evaluation, and the cloud completes time-space depth analysis. A multi-domain feature weighted fusion mechanism is innovatively adopted, the signal quality is improved in combination with a wavelet dual-threshold noise reduction algorithm, and quantization errors are corrected through a residual compensation module. When the edge node loads gt; and when 80%, dynamic resource scheduling is triggered, and resource allocation is optimized and calculated.

The invention relates to a goods shelf inspection algorithm based on an image recognition technology and a big data mining technology, and belongs to the technical field of digital inspection, and the algorithm comprises the following operation steps: 1, carrying out the data collection of a display picture of a goods shelf and a picture of a commodity, and obtaining the commodity data based on the picture; 2, preprocessing the acquired data image, and performing a denoising algorithm by using median filtering and mean filtering to reduce noise interference in the image; and step three, target detection and commodity information identification are carried out. And 4, detecting stockout, placement and labels of the commodities. And a fifth step of performing sales data analysis and generating optimization suggestions. And 6, generating a report according to an abnormal detection result, a data analysis result and an optimization suggestion. The problems of high cost, low efficiency and subjectivity in the traditional manual inspection and the existing technical scheme are solved. And the shelf management level and the sales efficiency of the store are improved through data optimization.

The invention relates to the field of image processing, and particularly discloses a rural highway pavement disease intelligent identification and positioning system comprising an image standardization module used for obtaining a standardized grayscale image; the wavelet decomposition module is used for obtaining a low-frequency sub-band and a plurality of high-frequency sub-bands; the high-frequency processing module is used for carrying out soft threshold processing on the high-frequency coefficient and retaining high-variance region features; the inverse transformation module is used for reconstructing the de-noised image; the edge enhancement module is used for highlighting crack and pit slot target contour information; the binarization module is used for segmenting a foreground disease candidate region; and the edge filling module is used for separating the disease from the background. According to the method, the core contradiction between background impurity removal and disease feature retention in rural highway tiny disease recognition is effectively solved, a traditional denoising algorithm either smooths tiny disease features or cannot thoroughly remove background impurities, and the system achieves the balance of the background impurity removal and the disease feature retention through cooperation of multiple modules.

The invention provides an audio signal denoising method based on improved variational mode decomposition and a related product, and the method comprises the steps: obtaining a target input audio signal, and searching the number of target components and a target penalty factor of variational mode decomposition through employing an improved particle swarm optimization algorithm introducing a local optimal solution jump-out mechanism and fuzzy entropy; decomposing the target input audio signal according to the number of the target components and the target penalty factor to obtain a plurality of first target components; calculating the fuzzy entropy of each first target component, and carrying out the noise reduction of the first target component of which the fuzzy entropy is higher than a preset fuzzy entropy threshold value through employing a wavelet threshold value denoising algorithm, and obtaining a first component; and obtaining a target output audio signal according to the first target component of which the fuzzy entropy is not higher than a preset fuzzy entropy threshold and the first component. Through the method and the device, the problem of poor denoising effect caused by unreasonable parameter setting in the prior art can be solved, so that more efficient and accurate audio signal denoising processing is realized.

The invention discloses a method and a system for correcting wrong words in minority language text image recognition, and the method comprises the steps: obtaining a minority language text image, removing the high-frequency noise of the minority language text image through employing a preset adaptive threshold denoising algorithm based on wavelet transformation, and generating a denoised image. The morphological contour features of at least one character are extracted from the denoised image, the morphological contour features comprise stroke thickness, stroke number and stroke order direction, the features are input into a support vector machine classification model, the language category of the denoised image is determined, and a language classification result is generated. And loading a corresponding character segmentation model and a corpus according to a language classification result, carrying out character-by-character segmentation and pattern matching identification on the denoised image, and generating a preliminary identification result and an identification confidence score of each character. According to the method for correcting the wrong words in the text image recognition of the minority language, the accuracy of character recognition of the minority language and the capability of correcting the wrong words are improved, and recognition challenges in complex scenes can be coped with.

The invention provides a composite insulator internal defect identification method and system, and the method comprises the steps: collecting an ultrasonic detection signal in a detection region selected on the outer surface of a composite insulator through an ultrasonic probe, and constructing a detection signal sample set; preprocessing the signals in the detection signal sample set by adopting a wavelet transform denoising algorithm to obtain preprocessed signals; carrying out empirical mode decomposition on the preprocessed signal, extracting characteristic parameters in the signal, and constructing a characteristic data set after carrying out dimension reduction processing on the characteristic parameters; and analyzing the feature data set by using a random forest algorithm to obtain an identification result of the internal defect of the composite insulator. Therefore, the problems that an existing manual defect identification method is high in subjectivity, more in misjudgment and the like are solved, a guiding function can be provided for factory detection, defect prevention and batch replacement of the composite insulator by means of a defect identification result, the defect identification precision is high, the anti-interference capacity is high, and automatic nondestructive detection on the interior of the composite insulator is achieved.

The invention discloses a medical image semantic segmentation method based on attention mechanism optimization, and relates to the technical field of medical image processing, and the segmentation method comprises the specific steps: S100, data collection and label preprocessing: collecting medical image data from different medical institutions and a plurality of imaging devices, according to the method, the attention mechanism is introduced to carry out deep preprocessing on the medical image data, the precision and efficiency of semantic segmentation of the medical image are remarkably improved, the attention mechanism is utilized, key areas, such as diseased regions or tissue boundaries, in the image can be recognized and enhanced, meanwhile, noise and irrelevant information are effectively removed, and the accuracy of semantic segmentation of the medical image is improved. The refined preprocessing mode not only improves the quality of the image, but also provides a more accurate data basis for subsequent image detection and segmentation, and the method is also combined with a self-adaptive denoising algorithm, dynamic adjustment of contrast, brightness and color and a geometric transformation advanced preprocessing technology, so that the availability and diagnostic value of the image are enhanced.

The invention provides a Fresnel optical image target detection and identification method based on event stream data enhancement, and the method comprises the steps: carrying out the data preprocessing of collected Fresnel optical image event data through employing a denoising algorithm based on time-space continuity, and screening the original event stream data; converting the screened event stream data of the target area into pixel data under an image coordinate system by adopting a dual threshold limiting strategy of event quantity and accumulation time; existing image features are enhanced, and pixel-level fusion, feature-level fusion and decision-level fusion are carried out on event stream data of an event camera and RGB camera data; and the target detection module is used for performing target detection and recognition on the fused data by using a YOLO target detection model. According to the method, the detection of the Fresnel optical image target is realized, so that the detection and recognition capability of the Fresnel optical image target in a complex environment is improved, and the method is particularly suitable for scenes such as seaborne landing guidance and the like.

The invention relates to a switch cabinet partial discharge signal positioning method based on a time difference positioning method, and belongs to the technical field of electrical engineering and signal processing. Aiming at the problem that the ultrasonic partial discharge signal has white noise, an improved wavelet threshold algorithm is adopted to construct a threshold function, and a continuous self-adaptive wavelet threshold denoising algorithm is adopted to carry out denoising processing on the partial discharge signal, so that the problems of excessive denoising and incomplete denoising existing in a traditional denoising algorithm are solved, effective separation of the signal and the noise is realized, and the denoising efficiency is improved. And the performance of the denoising algorithm is improved. In order to obtain a time delay value between each path of signals, a generalized cross-correlation time delay estimation algorithm based on PHAT-SCOT joint weighting is used for substituting a time delay estimation value into a switch cabinet partial discharge positioning equation set, and an optimal equation is obtained. According to the partial discharge optimal solution algorithm based on DE-PSO, the optimization equation is solved, the spatial coordinate position generating the partial discharge signal is positioned, the defects of parameter setting and threshold selection of the optimization algorithm are overcome, and the positioning precision of the partial discharge signal is improved.

A deep learning fan fault diagnosis method based on double wavelet fusion and CEEMDAN decomposition comprises the following steps: S1, collecting vibration signals of a fan motor driving end, a fan driving end and a non-driving end, and constructing a data set; s2, performing improvement on the basis of wavelet packet noise reduction to form a double-wavelet fusion noise reduction algorithm; s3, carrying out noise reduction processing on the acquired signals; s4, carrying out sample division on the noise reduction signals according to a fixed time window, and carrying out classification according to measurement points; s5, randomly dividing the samples into a training set, a verification set and a test set; s6, decomposing all the samples by using CEEMDAN, and extracting IMFs; s7, sending the sample into the time-frequency domain joint feature extraction model of the corresponding measuring point for modeling; s8, training to obtain a diagnosis model and exporting a weight file; and S9, loading the model detection test set, and comprehensively judging the operation state of the fan. According to the invention, the signal processing quality and the fault identification accuracy are improved.

The invention provides a comprehensive noise reduction performance evaluation method for a nonlinear ultrasonic detection signal noise reduction algorithm, and the method comprises the steps: firstly constructing a multi-working-condition noise reduction performance pre-screening mechanism based on error band analysis based on early-stage experimental data, and then introducing a radar map as an evaluation tool for the comprehensive noise reduction effect of the signal noise reduction algorithm. The noise reduction effects of a moving average method (MA), a spectral subtraction method (SS), a short-time Fourier transform method (STFT), a wavelet transform method (WT) and an orthogonal matching pursuit algorithm (OMP) are compared, and finally a signal noise reduction algorithm with the optimal comprehensive efficiency is screened out. On the basis of algorithm optimization, quantitative mapping rules between microcrack three-dimensional geometric parameters and relative nonlinear coefficients are analyzed through regression modeling, and the significant level of the correlation degree of the relative nonlinear coefficients and microcrack size parameters is effectively improved; and a high-confidence theoretical support is provided for quantitative nondestructive detection of the microcracks in engineering practice.

The invention relates to a heart and lung sound noise reduction method and system based on a sound stethoscope. A traditional heart and lung sound data noise reduction algorithm is poor in performance in a complex environment, so that a doctor cannot give an accurate diagnosis result. The heart and lung sound is acquired through the acquisition module, the heart and lung sound is converted into a digital form through the analog-to-digital converter and stored, then the stored heart and lung sound data is subjected to traditional time-frequency noise reduction processing and deep learning algorithm noise reduction processing, and the deep learning algorithm noise reduction processing comprises construction of a clean heart and lung sound data set and a noise data set. The heart and lung sound data with noise is obtained by randomly synthesizing clean heart and lung sound data and a noise data set, and the obtained data set is subjected to noise reduction and noise removal processing. According to the method, noise reduction and noise removal are further carried out on the sound signals by adopting the traditional and deep learning model algorithms, the signal-to-noise ratio is obviously improved compared with noise data and a traditional method, and cardiopulmonary sound extraction can be carried out in a more complex external environment.

The invention provides a Prony algorithm-based cable segment wave velocity acquisition method, device and system, and a medium, and the method comprises the steps: testing a to-be-tested cable, and obtaining a cable signal transfer function; on the basis of the cable signal transfer function, in combination with cable joint distribution, constructing a cable signal approximation function based on a Prony method; constructing a linear prediction model of the cable signal based on the cable signal approximation function; performing denoising processing on the linear prediction model based on a singular value decomposition method to obtain a denoised linear prediction model; solving the denoised linear prediction model to obtain an attenuation coefficient; and calculating the segmented wave velocity of the cable based on the attenuation coefficient. According to the method, the Prony estimation method is combined with the singular value decomposition noise reduction algorithm, high-precision extraction of attenuation constants and segmented wave velocity decoupling are achieved, and then the electrical distance positioning precision is improved.

The invention discloses an intelligent noise reduction processing method for wireless microphone chip integration, which relates to the technical field of intelligent noise reduction, is used for solving the problems that noise in a quiet recording environment is difficult to collect, a denoising algorithm is single and the like, and comprises the steps of performing matching identification on microphone models, confirming a connection range and a sound source collection range of the microphone, and performing noise reduction. And integrating the connection range of the microphone and the sound source acquisition range, and calculating a sound acquisition position by using a K-nearest neighbor algorithm. The method comprises the following steps: determining a real-time position of a user, collecting a sound source, obtaining microphone output voltage, calculating microphone sensitivity and frequency response, comparing recording adaptation sensitivity of a noise file by using a logistic regression algorithm according to the microphone sensitivity and the frequency response, and selecting the noise file for playing. And acquiring sound source data of the noise file through a microphone, calculating a noise file preceding SNR value at a sound collection position, comparing the noise file preceding SNR value with the SNR stored in the recording database, analyzing environmental noise, selecting different algorithms according to the environmental noise to carry out de-noising processing, and storing the data.

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 belongs to the field of power equipment state monitoring, and relates to a generator partial discharge on-line monitoring method, system, equipment and medium, and the method comprises the following steps: obtaining four types of original signals of a generator; extracting a partial discharge feature significant signal layer from the original signal by using an adaptive hierarchical denoising algorithm; constructing a feature quantity library based on the partial discharge feature significant signal layer, and extracting the frequency spectrum gravity center and wavelet energy entropy of each type of original signals; performing fuzzy logic weighting on the frequency spectrum gravity center and the wavelet energy entropy of each type of original signals to obtain a primary fusion result of each type of original signals; performing deep fusion on the primary fusion result of each type of original signals by adopting an LSTM neural network to obtain a partial discharge primary judgment result; and obtaining the score of the partial discharge preliminary judgment result based on the abnormal degree score of the mahalanobis distance, and if the score is greater than a decision threshold, determining that partial discharge occurs. The partial discharge of the generator can be quickly and effectively judged, and field operation and maintenance personnel can respond to the partial discharge conveniently.

The invention provides a quick image recognition method for the water quality condition in a water supply pipeline, and the method is characterized in that a high-definition camera with a light source is used for obtaining a color image in the underground water supply pipeline, the color image is converted into a gray image through an OpenCV method, the gray image is converted into a high-resolution image through a super-resolution method, and the high-resolution image is used for recognizing the water quality condition in the underground water supply pipeline. Carrying out segmentation and feature enhancement on a distribution region of suspended particulate matters in the high-resolution image by applying an average signal-to-noise ratio, a local self-adaptive thresholding algorithm and a denoising algorithm, extracting a region related to a water quality turbidity parameter, comparing a feature region containing the water quality turbidity parameter with a standard water quality turbidity grade image database, and obtaining a water quality turbidity grade image; and judging the turbidity grade of water in the in-service water supply pipeline. According to the invention, rapid identification and real-time monitoring of the pipeline water quality turbidity grade in the water supply network are effectively realized, and a new method is provided for rapid identification and condition monitoring of the water quality of the water supply network.

The invention belongs to the field of image processing and computer vision in computer intelligent information processing, and discloses an industrial product unsupervised anomaly detection method and system based on a conditional control diffusion model, and the method comprises the steps: constructing a diffusion model based on conditional control and a multi-scale double-attention mechanism, reconstructing the to-be-detected sample image by using a diffusion model based on condition control and a multi-scale double-attention mechanism to obtain a reconstructed image with the same size as the to-be-detected sample image; and extracting feature maps of different scales from the to-be-detected sample image and the reconstructed image through the same pre-trained feature extraction model. Performing a two-stage background denoising algorithm on each scale feature map to obtain an initial abnormal feature map; calculating an average value of a plurality of initial abnormal feature maps obtained by using the same method to obtain a final abnormal feature map; and processing the final abnormal feature map by adopting a thermodynamic diagram generation algorithm to generate a final abnormal thermodynamic diagram, thereby realizing the anomaly detection of the to-be-detected sample image.

The invention discloses a method and a system for detecting internal defects of a bathroom glass door based on multispectral imaging. The method comprises the following steps: carrying out multi-view and multiband image acquisition on the bathroom glass door by adopting a combined spectrum light source; performing adaptive reflection correction on the acquired image to generate a multi-band basic image; optimizing the multi-band basic image through a hybrid denoising algorithm to generate a multi-band detection image; performing multi-modal feature extraction and fusion on the multi-band detection image to obtain a multi-modal fusion feature vector, inputting the multi-modal fusion feature vector into a multi-feature fusion model combining a convolutional neural network and a support vector machine, and identifying and grading defect types by adopting a dual-stage feature enhancement-classification architecture; and outputting a defect type and severity grading result to a terminal in real time. The method is used for solving the technical problems of visual angle and spectrum blind areas, insufficient interference suppression capability, wave band crosstalk, feature deficiency and poor small sample generalization in existing bathroom glass door defect detection.

A static human pose estimation method based on channel state information (CSI) signal angle of arrival (AoA) estimation is disclosed. A signal angle of arrival in a sensing area is estimated from CSI and an image is constructed, and a human pose is estimated from the image using a teacher-student network. The method comprises the following specific steps: CSI signals in a sensing area are collected at different heights using a receive antenna column with a moving track. Two-dimensional AoA image features are constructed: CSI information is converted into one-dimensional AoA data by using a MUSIC algorithm, and the one-dimensional AoA data of different heights are combined into a two-dimensional AoA image, and an environmental denoise algorithm is designed. A teacher-student network model is constructed, and a student network based on the two-dimensional AoA image is supervised, wherein the student network model can independently estimate a human pose from CSI.

The invention relates to the technical field of infrared imaging, in particular to an infrared image self-adaptive denoising algorithm and system based on an FPGA (Field Programmable Gate Array), and is characterized in that an adaptive blind pixel searching algorithm module captures data of a certain frame number at an FPGA end, performs multi-directional gradient detection on each pixel point, sets conditions to judge to obtain a blind pixel position, and marks the blind pixel position; the convolution kernel self-adaptive blind pixel replacement module performs self-adaptive blind pixel replacement multi-time module multiplexing at the PL end of the FPGA through a convolution kernel sliding block and gradient comparison to ensure complete coverage of a large blind pixel group, and the self-adaptive median filtering module intelligently selects and replaces pixels meeting requirements through automatic sorting of center pixels and surrounding pixels; according to the method, surface fuzzy filtering is carried out through a surface-blue algorithm module, so that a smooth area in an image is effectively smoothed, details of an edge area are reserved, blind pixels in a video stream can be effectively detected and compensated, and meanwhile traditional noise is suppressed and image details are reserved through multi-stage filtering.

The invention relates to the technical field of computers, and discloses an object classification method and device based on deep reinforcement learning under a complex noise condition. The method comprises: acquiring to-be-classified data; performing noise classification on the to-be-classified data based on a noise classification model obtained by deep reinforcement learning to obtain noise classification corresponding to the to-be-classified data; a noise reduction algorithm corresponding to the noise classification is called to carry out noise reduction processing on the to-be-classified data, and the denoised data is obtained; performing object classification on the denoised data based on an object classification model obtained by deep reinforcement learning to obtain an object classification result; the problem that a traditional denoising algorithm is poor in noise removal effect can be solved. According to the method, the noise reduction algorithm can be adaptively switched to perform noise reduction processing under the condition of more noise types, so that the noise reduction effect of the to-be-classified data in a complex noise scene is improved.