128 results about "Adaptive denoising" patented technology

The invention relates to the technical field of building information modeling, in particular to a building drawing geometric feature extraction and visual modeling method and system, and the method comprises the steps: carrying out the self-adaptive noise reduction, contrast enhancement and line refinement processing of an original building drawing image, and carrying out the automatic layer separation based on colors and line types; linear geometric features and specific symbol geometric features in the drawing image are extracted, and a geometric feature set is constructed; component instantiation, attribute assignment and topological relation reasoning are carried out by using a predefined building component semantic rule base, and a building component semantic network is generated; and mapping the semantic network to a parameterized three-dimensional modeling engine, calling an IFC standard three-dimensional template, performing parameter driving and automatic assembly, generating a three-dimensional building model with semantic information and a spatial structure, and performing visual output. According to the method, efficient and standardized conversion from a two-dimensional building drawing to a three-dimensional building model can be realized, and the method has the remarkable advantages of processing complex drawings and high-precision modeling.

The invention provides an intelligent event identification method and system based on a high-speed camera, and the method comprises the steps: setting the collection parameters of the high-speed camera, and triggering the camera to collect a target scene video stream. And hardware acceleration decoding processing is carried out on the collected original video data stream, real-time environment illumination information of the environment illumination sensor is obtained, and dynamic brightness equalization processing is executed. And performing motion adaptive denoising processing on the video sequence. Geometric distortion correction is carried out on the image sequence through camera calibration parameters, sub-pixel-level displacement vectors and dense optical flow field data of a moving object are extracted, and multi-scale morphological features are extracted. And the features are fused to generate motion feature data, the data are processed through a spatio-temporal joint event classification model, an event identification result is output, the result is bound with a high-precision timestamp, and event identification information is output to an industrial control system display device in real time. According to the invention, the accuracy and real-time performance of event identification can be improved.

The invention belongs to the technical field of artificial intelligence, and relates to an artificial intelligence-based electric energy quality disturbance identification and positioning method, which comprises the steps of constructing an electric energy quality disturbance signal data set, performing segmented preprocessing on electric energy quality disturbance voltage data, enhancing time-frequency joint features and encoding disturbance sensitive areas. And constructing a deep learning model for power quality disturbance identification and positioning, and identifying and positioning the power quality disturbance. According to the invention, through adaptive denoising processing, boundary detection and multi-resolution time-frequency feature extraction, the identification precision and positioning precision of power quality disturbance are significantly improved; self-adaptive wavelet denoising and dynamic segmentation are combined, noise interference is effectively suppressed, and the edge characteristics of voltage sudden change points are kept; according to the dual-task sharing network, disturbance identification and positioning tasks are cooperatively optimized, so that the network can consider disturbance classification and time positioning at the same time; and through Bayesian reasoning, the system can output confidence estimation, provides credibility quantification of identification and positioning results, and effectively improves the reliability of the system.

The invention provides a high-noise-resistance bearing fault diagnosis method for complex working conditions, relates to the technical field of bearing fault detection, and constructs a progressive technical system of'dynamic noise reduction-multi-modal fusion-cross-domain association '. According to the method, firstly, working condition self-adaptive noise reduction is achieved through sliding window dynamic principal component analysis, and redundant noise caused by rotating speed fluctuation is stripped while periodic impact characteristics are reserved; meanwhile, on this basis, a time domain-frequency domain multi-mode complementary feature system is constructed, and the denoised signals are respectively input into a bidirectional gating circulation unit and an improved residual network added with FFT transform to extract bidirectional time sequence dependence and frequency domain texture features. And finally, performing feature enhancement on a time sequence signal and a time-frequency image extracted by the multi-modal feature network by adopting eight self-attention, constructing a dynamic interaction channel by taking a time sequence feature as Query and an image feature as Key-Value, and adaptively screening a cross-modal association feature most related to the context of the current rotating speed.

The invention relates to the technical field of radar electronic countermeasures, and particularly discloses a radar radiation source open set identification method (ARPLAD) based on countermeasure reciprocity point learning. The method comprises the steps that adaptive noise reduction and feature extraction are conducted on radar signals through a feature extraction network fusing a DRSN module and an ECA module, channel-level adaptive noise reduction is achieved through the DRSN by means of a soft threshold function, and key features are strengthened through dynamic weight distribution by means of the ECA; an adversarial reciprocity point learning framework is adopted, reciprocity points are set for each known class to serve as out-of-class space representation, and known and unknown class feature spaces are separated by maximizing the distance between known class samples and the corresponding reciprocity points; introducing a central loss function to compress intra-class feature distribution, and constructing a weighted total loss function optimization network in combination with an open space limitation function; and calculating a class self-adaptive threshold value based on the training sample, and comparing the maximum distance from the sample to each reciprocity point with the threshold value during testing to judge known and unknown classes.

The invention discloses a vocal cord problem recognition and feedback system for the ophthalmology and otorhinolaryngology department. The vocal cord problem recognition and feedback system comprises a sound collection module, an image collection module, a biological feedback module, a data processing module, an AI diagnosis module and a rehabilitation guidance module. The sound acquisition module comprises a microphone array and a self-adaptive noise reduction unit; the image acquisition module is provided with an endoscope camera and an image enhancement processor; the biological feedback module integrates a laryngeal myoelectricity sensor and a three-dimensional motion simulator; the data processing module executes multi-modal feature extraction and fusion; through mutual cooperation of the sound acquisition module, the image acquisition module, the biological feedback module and the data processing module, data can be accurately acquired, the early canceration detection rate is improved and the misdiagnosis rate is reduced through multi-modal fusion, the data acquired by the multi-modal structure is analyzed through the data processing module, the model is combined with a weekly updated disease map, and the early canceration detection rate is improved. And the recurrence prediction accuracy is improved.

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 an optical cable fault positioning method based on OTDR signal characteristic adaptive denoising and event identification, and the method comprises the steps: classifying modal components after OTDR signal adaptive noise decomposition based on a preset entropy threshold value, and obtaining a noise dominant component, a mixed component and a signal dominant component; singular value difference spectrum abrupt change point detection is carried out on the noise dominant component, and residual useful signals are extracted; verifying the entropy value of the mixed component, and reconstructing the component meeting the entropy threshold value, the signal dominant component and the residual useful signal into a de-noised signal; synchronously optimizing hyper-parameters and feature selection subsets of the support vector machine by adopting a swarm intelligence algorithm, wherein search parameters of the algorithm are dynamically updated according to an exponential decay mechanism; and outputting fault point space position information based on the optimized support vector machine model.

The invention discloses a multi-modal data adaptive denoising and missing reconstruction method and system, and relates to the technical field of point data denoising and reconstruction, and the method comprises the steps: obtaining to-be-processed multi-modal original data and a modal missing mask; performing unsupervised denoising on image data in the multi-modal original data to obtain a denoised image, and further obtaining multi-modal data; inputting the multi-modal data into a double-flow encoder for processing to obtain a multi-modal embedded vector of cross-modal alignment; the method comprises the following steps of: performing mapping and adding position embedding on a modal embedding vector to obtain each modal coding feature, determining a missing modal based on a modal missing mask, inputting an available modal coding feature into a retrieval enhanced expert model based on prototype memory to perform missing reconstruction to obtain a multi-modal joint representation, and mapping the multi-modal joint representation to a task output space through a full connection layer. Through introduction of unsupervised denoising, double-flow coding alignment and modal knowledge expert hybrid reconstruction, robust representation learning and information complementation under the condition that noise and modal missing exist in multi-modal data are realized.

The invention discloses a food waste detection method and system based on image processing, belongs to the field of image recognition, and aims to realize efficient and automatic recognition and quantification of kitchen waste. According to the method, residual food images are collected at multiple periods and multiple angles in a kitchen garbage can or a dinner plate recovery area through high-resolution and multi-spectral imaging equipment, preprocessing is carried out in combination with an improved Retinex algorithm and a space self-adaptive denoising technology, and the image quality is improved. Afterwards, fine segmentation of a food area is achieved through a multi-scale super-pixel segmentation and graph segmentation algorithm, and multi-category intelligent recognition is conducted on remaining food through a recognition network fused with multi-modal features. The system further combines stereoscopic vision and Monte Carlo sampling to dynamically and accurately count the volume or weight of various residual foods. The method has the advantages of high adaptability and accurate statistical result, and can provide data support for catering management, resource recovery, nutrition evaluation and the like.

The invention relates to the technical field of image stitching, and discloses a video transmission high-definition image intelligent stitching method and system. The system comprises an image preprocessing module, a feature point matching and screening module, an image optimal splicing seam generation module and a video output module. The method comprises the following steps: firstly, acquiring a video, carrying out dynamic range equalization, carrying out filtering processing by using an adaptive noise reduction method, and carrying out image correction; secondly, extracting feature points by using a self-adaptive corner detection algorithm, matching the feature points based on a multi-dimensional spatial data index to obtain feature point matching pairs, and screening the feature points; searching an overlapping region, and introducing a dynamic search algorithm to generate an optimal image splicing path to obtain an optimal image splicing seam; and finally, dividing the image according to the dynamic grid, and realizing video output by using priority ranking. According to the method, the video images are processed and spliced, the purpose of intelligent image splicing is achieved, and the method is accurate and objective.

The invention provides a weld defect detection method and system fusing TOFD image waveform characteristics. The method comprises the following steps: carrying out adaptive noise reduction processing on TOFD image data and TOFD waveform data; performing feature extraction on the TOFD image data and the TOFD waveform data after adaptive noise reduction processing through a dual-network view angle to obtain image multi-scale features and waveform time sequence features; fusing the image multi-scale features and the waveform time sequence features by adopting a self-adaptive attention weighting mode to obtain fused features; and performing defect detection based on the fusion features. According to the method, film evaluation knowledge of'simultaneous analysis of wave maps' in the field of nondestructive TOFD detection is fused, TOFD image weld defect detection and identification are realized, and the accuracy and reliability of a defect detection result are improved.

The invention relates to the technical field of artificial intelligence image processing and biological detection crossing, and discloses a perception enhancement and feature distillation-oriented high-density microalgae detection method, which comprises the following steps: acquiring a high-density microalgae image containing cell overlapping, boundary blur and cross-scale distribution, and inputting the image into a backbone network to extract a multi-scale feature map; optimizing the bounding box through potential consistency mapping processing; generating a microalgae density map through density sensing auxiliary processing, and calculating density loss; self-adaptive denoising is carried out based on image complexity; a student model is optimized by using a dual-feature distillation framework, and the small-scale microalgae detection capability is enhanced; and finally, fusing the results of the modules, and outputting the position, category and quantity of the microalgae. According to the method, the overall average accuracy of high-density microalgae detection can be improved, bounding box jitter and small-scale microalgae omission ratio are reduced, the average reasoning time is shortened, the real-time detection requirement is met, and reliable data support is provided for microalgae culture process control and optimization.

The invention discloses a student psychological risk perception method based on multiple modes, and relates to the technical field of emotion calculation and intelligent education. The method comprises the following steps: firstly, extracting a facial expression feature vector and a voice intonation feature vector respectively by using a convolutional neural network and Fourier transform through a collected video stream and an audio stream; then adaptive denoising processing is carried out on environmental interference, timestamp alignment and dynamic time warping are carried out on the denoised multi-modal data, time sequence synchronization is ensured, and corrected multi-modal sequence data are formed; then, dynamic emotion track features are extracted from the sequence data, a preliminary emotion state label is generated by comparing the dynamic emotion track features with a baseline threshold value, and the threshold value is adaptively updated in combination with historical data so as to improve the judgment accuracy; and finally, aggregating the emotional state labels of a plurality of students to generate a visual group emotional thermodynamic diagram so as to realize macroscopic perception of group psychological risks. The accuracy, robustness and visualization degree of student psychological state analysis are effectively improved, and an efficient technical means is provided for campus psychological early warning.

The invention discloses an infrared visible light image fusion system based on semantic-driven space-frequency routing, and aims to solve the problems of multi-mode redundancy, weather degradation and insufficient space-frequency modulation. The core of the method comprises the following steps: a CMBA module realizes cross-modal bottom layer interaction and redundancy elimination; the FASRP module is integrated with 2D-SSM to perform long-range modeling, and high and low frequency feature decoupling is realized by using a routing mask; the DSPE module extracts environment degradation semantic priori by relying on a visual language large model; the DFMB module decouples the features into amplitude and phase, and realizes adaptive denoising according to prior dynamic gating. And the system reconstructs the image through fidelity fusion and progressive decoding. According to the method, the generalization ability in a complex scene is improved, and the fused image has precise infrared target positioning and visible light texture details.

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 a point cloud denoising method of a photon counting laser radar and an intelligent denoising system, and belongs to the technical field of photon laser radar data processing. The technical problems that the performance of a fixed parameter denoising method is seriously unbalanced under different ground feature and complexity scenes, and high denoising rate and high signal fidelity cannot be considered at the same time are solved. Synchronously acquiring an optical image and an original photon point cloud of the target area; identifying the dominant ground feature type of the laser spot area through real-time semantic segmentation; calculating scene complexity based on the point cloud data and determining the grade of the scene complexity; querying a preset two-dimensional parameter mapping table according to the combination of the surface feature type and the scene complexity level, and dynamically obtaining an optimal denoising parameter set; and finally, the parameter set is utilized to drive adaptive denoising processing, and signal points and noise points are distinguished by calculating the local density and direction consistency of the points and constructing a two-dimensional discrimination space. The method is mainly used for real-time processing platforms such as unmanned aerial vehicles, and the point cloud denoising precision, the signal retention rate and the topographic feature fidelity are remarkably improved.

The invention discloses a power transmission line unmanned aerial vehicle inspection depth visual intelligent identification method and system, and relates to the field of power transmission line inspection, and the method comprises the steps: dynamically adjusting imaging parameters based on the voltage grade and environment complexity of a power transmission line, collecting the multispectral visual data of a line body, a metal fitting for line connection and fixation, and a gallery environment, according to the invention, the method can dynamically adapt to the voltage grade of the power transmission line and the environmental complexity, optimizes the imaging effect, accurately collects the multi-dimensional visual data, and associates the key environmental parameters. The data standardization level is improved through adaptive noise reduction, accurate splicing and distortion correction, defect features and environment association attributes are efficiently mined through an attention guidance mechanism and a dynamic convolutional network, and accurate judgment of defect types and levels and future evolution trend prediction are performed in combination with historical data.

The invention relates to the technical field of graph contrast learning, in particular to a graph contrast learning recommendation method based on adaptive denoising enhancement and difficult negative sample generation. According to the method, a dynamic denoising mechanism is introduced to automatically and accurately recognize and filter noise edges, meanwhile, key information interaction in an original image is effectively reserved, linear mixing is carried out on embedding of an original view and a denoised view, then a loss function is optimized through two-stage joint, and the image quality is improved. The whole model is uniformly trained in combination with double-view representation and high-quality negative samples while noise is removed, and finally, the model can sensitively capture subtle differences between positive and negative samples through high-quality difficult negative samples generated adaptively, so that the discrimination ability of comparative learning is remarkably improved, and the accuracy of comparative learning is improved. The method not only strengthens the recognition capability of the model for the negative sample, but also optimizes the contrast learning target, effectively improves the overall learning effect and generalization capability, and provides a powerful guarantee for the accuracy of a personalized recommendation algorithm.

The invention relates to the technical field of image processing, in particular to a GIS shell production line real-time monitoring method and system. The method comprises the following steps: acquiring a surface image of the GIS shell and carrying out graying processing on the surface image; and carrying out adaptive denoising processing on the grayscale image. The self-adaptive denoising processing comprises the following steps: for each target pixel point, determining the noise attribute of the target pixel point based on the gray value and neighborhood relationship of the target pixel point; identifying a key area representing the stress concentration part of the GIS shell in the image, and determining the position importance of the key area according to the spatial proximity and edge feature consistency of a target pixel point and the key area; and in combination with the noise attribute and the position importance, adaptively adjusting the filtering intensity in the filtering processing. According to the method, unique filtering intensity is obtained for each target pixel point, efficient denoising is achieved, details are kept, and the accuracy and reliability of defect detection are improved.

The invention provides a bidirectional LSTM photovoltaic power generation prediction method based on wavelet decomposition and double attention, and belongs to the technical field of power generation prediction. Selecting an optimal wavelet basis function by using a particle swarm optimization algorithm to carry out adaptive noise complete set empirical mode decomposition on the power sequence to obtain a multi-layer intrinsic mode function component, and carrying out adaptive denoising and power signal reconstruction according to a multi-scale permutation entropy and a Bayesian risk minimization criterion in combination with meteorological conditions; key feature variables are extracted through a maximum information coefficient, a bidirectional long-short-term memory network prediction framework is established, a feature attention mechanism and a double-path time attention structure are introduced, and when power mutation or irradiance mutation is detected, a sparse attention weight rapid reconstruction mechanism is triggered to complete prediction. The technical problem that the prediction precision is reduced when the photovoltaic power generation power changes suddenly under the cloudy weather condition is solved.

The invention provides a hyperspectral data optimization method and device, equipment and a storage medium, and relates to the technical field of image processing. The method comprises the following steps: acquiring to-be-processed hyperspectral data, and performing multi-scale feature extraction on the to-be-processed hyperspectral data to obtain a multi-scale feature vector; calculating the fuzzy correlation degree between different wavebands of the to-be-processed hyperspectral data, performing waveband screening according to the fuzzy correlation degree to obtain a reserved waveband, and performing dimensionality reduction on the to-be-processed hyperspectral data according to the reserved waveband to obtain dimensionality-reduced hyperspectral data; dividing the dimension-reduced hyperspectral data into a plurality of local areas, and performing adaptive denoising on each local area according to local data features of each local area to obtain denoised hyperspectral data; and performing data reconstruction according to the multi-scale feature vector and the denoised hyperspectral data to obtain optimized hyperspectral data. According to the method, the hyperspectral data can be optimized more efficiently and accurately, and the quality and the application value of the hyperspectral data are improved.

The present application belongs to the field of seismic data processing and data optimization, in particular to a method and device for seismic prestack data optimization based on improved BEMD algorithm. The method of the present application uses improved BEMD algorithm and adaptive denoising algorithm to decompose prestack gathers into characteristic signals of different scales. Then, orthogonal wavelet transform denoising based on threshold is carried out on each component to remove most of the noise. Then, the correlation coefficient between each component and the original data is calculated, and the data is reconstructed based on the correlation coefficient. The effective signal is retained to the greatest extent, the interference of noise signal is removed, the signal-to-noise ratio of prestack gathers is improved, and a good data basis is provided for subsequent seismic prediction algorithms.

The invention discloses a self-adaptive noise reduction method for a vibration signal of a filling pipeline in the technical field of signal denoising. The self-adaptive noise reduction method comprises the following steps: decomposing the vibration signal of an original pipeline by adopting an optimized VMD to obtain a plurality of IMF components; identifying a signal dominant component and a noise dominant component in the IMF component; noise reduction is carried out on the noise dominant component by adopting an improved wavelet threshold function; and reconstructing the noise dominant component and the signal dominant component after noise reduction to obtain a denoised filling pipeline vibration signal. According to the method, the optimal modal number K and the penalty factor alpha of the VMD are adaptively determined by adopting an NSGA-II multi-target optimization algorithm, the subjectivity of parameter adjustment by traditional artificial experience is overcome, and meanwhile, the problems that a traditional single-target optimization method is prone to falling into local optimum, weak in global search capability and one-sided in optimization are solved; and meanwhile, a wavelet threshold function is cooperatively improved, so that medium-high-frequency noise can be eliminated, low-frequency interference can be suppressed, and the signal quality and the feature identification degree are greatly improved.

The invention discloses a method and a system for measuring the layer thickness of a multilayer hollow pipe fitting based on image recognition, and relates to the technical field of multilayer hollow pipe fitting detection, and the technical key points are as follows: firstly, enhancing boundary features through multi-band image fusion preprocessing, and offsetting dimensional deviation by combining dynamic environment compensation and residual stress correction; through multi-dimensional verification and error correction, the measurement precision reaches a preset high-precision standard and is obviously superior to that of traditional vernier caliper measurement and ultrasonic measurement; secondly, a stable chromatic aberration boundary is constructed through a color master adaptation rule, and the influence of material pores, defects and distribution uniformity is avoided; meanwhile, through the technologies of self-adaptive noise reduction, dynamic measurement density adjustment and the like, the influence of factors such as ambient light interference and boundary complexity is resisted, and the measurement reliability is ensured; in addition, the color masterbatch adaptation rule can be dynamically adjusted according to optical characteristics and forming process parameters of different materials, the method is suitable for various complex multilayer hollow pipe fittings, and a measurement scheme does not need to be redesigned for specific pipe fittings.

The present application belongs to the technical field of electric power construction, and particularly relates to a power transmission and distribution construction monitoring method and system based on air-ground communication. The present application obtains an initial image sequence of a construction site through image acquisition equipment deployed in cooperation with the air and the ground, first performs frequency domain and spatial domain joint adaptive denoising on the image, and then identifies the construction process node in real time; the feature anchor points of the intra-frame static ground features are extracted, rigid alignment is performed through feature anchor point matching, and a standardized image sequence that is spatially aligned throughout the construction period is generated; a monitoring target area that matches the process node is demarcated, the gray scale gradient and edge texture feature vectors in the area are extracted, and are transmitted to the management and control end through an air-ground communication link, and are compared with the standardized construction feature benchmark corresponding to the process, to complete accurate judgment of safe operation. The present application solves the pain points of construction scene image noise and spatial misplacement, and greatly improves the monitoring recognition accuracy and management and control reliability.

The invention provides a self-adaptive denoising method based on Meyer wavelets and an improved smooth threshold function in order to solve the problem that a transformer partial discharge high-frequency current sensor field calibration signal is seriously polluted by field electromagnetic noise interference. The method comprises the following steps: firstly, arranging a standard sensor according to an optimal cross-core distance to obtain an original noisy signal; then adaptively determining a decomposition series according to a data length, selecting Meyer wavelets to perform multi-scale decomposition, performing smooth contraction on wavelet coefficients by adopting a continuously derivable, progressive and unbiased improved threshold function, and finally reconstructing a high signal-to-noise ratio verification signal through wavelet inverse transformation; the method does not need manual intervention of the threshold value, keeps orthogonality and high-order derivability in the whole process, and is beneficial to improving the accuracy of the verification result of the high-frequency sensor to be tested.

The application belongs to the field of intelligent ticket management, and specifically relates to a financial ticket information automatic input system based on artificial intelligence, which comprises a ticket collection module, a multi-scale self-adaptive noise reduction module, an intelligent classification module and an intelligent partition input module; the application adopts a feature double-domain self-adaptive enhancement method based on transpose self-attention, improves noise reduction robustness, specifically processes different noise types, overcomes the limitation of a single domain, restores the global structure while retaining the details, increases the noise reduction capability of the ticket image, and improves the text detection efficiency; the application adopts a document intelligent partition method, unifies heterogeneous tasks into the combination of instance and semantic segmentation, avoids model redundancy, maps the category name into a semantic query, supports open set classification and zero sample migration, realizes the dynamic interaction of instance and semantic query through hybrid query, enhances the understanding of the model to the complex document structure, and realizes efficient ticket image partition information extraction and input.

The application discloses a kind of based on adaptive noise reduction tool damage gray image intelligent classification method, mainly including a network consisting of adaptive noise reduction module and a tool damage image classification module;Noise reduction module and classification module are simultaneously accepted end-to-end joint training, share and optimize network parameters, while increasing a balance parameter in loss function, the parameter is according to the classification result of feedback of back propagation algorithm adaptive optimization noise reduction level, finally reach the optimal classification performance.The application can automatically identify whether the image contains noise and batch solve the classification problem of image noise for tool damage gray image, improve the prediction ability of model for difficult samples through adaptive noise reduction mode, can effectively remove image noise, while reducing the amplification effect of corresponding loss function to anti-noise, finally improve the intelligent classification ability of tool damage gray image from image processing quantity, image quality and prediction efficiency.

The invention relates to an intelligent aiming design method and system based on deep learning. According to the method and system, infrared and visible light bimodal video streams are synchronously collected, and weather types and grades are recognized in real time in combination with meteorological sensor data; carrying out image enhancement by adopting a physical constraint self-adaptive denoising network; constructing a target trajectory by using a target detection and tracking algorithm; high-precision prediction of a target motion track is realized based on a double-flow neural network and a time sequence prediction model; fusing the real-time meteorological data and the trajectory model to carry out trajectory compensation calculation; finally, the dynamic aiming point coordinates are calculated; according to the method, the technical problems of serious image degradation and misalignment of target prediction in severe weather are effectively solved, and the first hit rate and task efficiency of an aiming system in a complex environment are remarkably improved.