582 results about "Category recognition" patented technology

The invention discloses an intelligent control system and method for the rotating speed of a centrifugal machine, relates to the technical field of centrifugal machine control, and solves the technical problem that in the prior art, when the rotating speed of the centrifugal machine is adjusted, the influence of equipment abrasion on the centrifugal machine is neglected, so that the adjusting speed cannot meet the requirement, and the rotating speed control efficiency of the centrifugal machine is low. Inputting the detection image into a material category identification model to obtain a category label; searching an initial rotating speed according to the category label and the material weight; calculating an equipment wear coefficient according to historical equipment data, calculating an equipment adjustment rotating speed according to the equipment wear coefficient, and assigning the equipment adjustment rotating speed to the equipment rotating speed; calculating a centrifugal machine abnormal coefficient after calculating a vibration abnormal coefficient according to the equipment data; the alarm signal is generated according to the abnormal coefficient of the centrifugal machine, equipment parameters of the centrifugal machine are accurately obtained through multivariate data, the rotating speed difference caused by equipment abrasion is considered, the rotating speed of the centrifugal machine is dynamically adjusted, the state of the centrifugal machine is monitored in real time, and the rotating speed control efficiency of the centrifugal machine is improved.

The present application provides a data processing method, a category identification method and a computer device, in which, firstly, node information is screened out based on node edge data and feature information, that is, K first nodes with rich structures and high feature discrimination are located, and then a feature similarity is calculated based on relevant information of the first nodes. On the other hand, since a calculation amount of the feature similarity of the node is relatively small, for a case that model training and prediction of node category are completed at the same time, a second node can include a node to be classified, and a model parameter used in category prediction is obtained by performing parameter iterative updating with taking the node to be classified as an unlabeled node.

The invention provides an underwater target identification method and system based on multi-source sensor data fusion. The method comprises the following steps: firstly, synchronously acquiring an original sound wave reflection signal and original optical image data of a target underwater area, then carrying out acoustic compensation processing on the original sound wave reflection signal to generate a target sound wave reflection signal, and meanwhile, carrying out optical compensation processing on the original optical image data to generate target optical image data; performing dynamic weighted fusion on the reliability measurement of the target sound wave reflection signal and the reliability measurement of the target optical image data through an adaptive fusion module to generate an enhanced multi-modal data set; and finally identifying the specific category of the underwater target through a multi-modal feature matching and decision-making mechanism. According to the technical scheme provided by the invention, the limitation of a single sensing mode is overcome, the quality of collected information is improved, the direct confirmation efficiency of common target recognition is also improved, and high accuracy and data reliability of target category recognition in a complex scene are ensured.

The invention discloses an emotion recognition method and system based on cross-modal alignment and matching enhancement. According to the method, firstly, feature extraction is carried out on text, audio and video modalities in a data set, and then a text and audio cross-modal emotion alignment module and a text and video cross-modal emotion alignment module are constructed respectively, so that cross-modal semantic alignment is realized. Constructing an emotion label matching module based on an alignment result, generating modal pairs with similar emotions but different labels by using a difficult negative sample mining strategy, and paying attention to cross-modal emotion consistency through a dichotomy task guide model; performing modal feature fusion on the three modals through a six-layer attention crossing mechanism, finally splicing feature vectors, inputting the spliced feature vectors into a long-sequence context fusion modeling module for deep modal fusion, and capturing cross-modal interaction information; and the fused features are sent to an emotion classification module, and a final emotion category recognition result is output.

The invention discloses a seabed sonar image recognition system based on a graph neural network, and the system comprises the following modules: an image collection module which is used for collecting an original sonar image and constructing an original sonar image data set; the preprocessing module is used for generating a de-noised sonar image data set; the three-dimensional landform structure modeling module is used for constructing a three-dimensional landform structure diagram; the graph structure coding module is used for obtaining multi-scale landform semantic feature representation; the decoding and evaluation module is used for reconstructing a preliminary seabed landform semantic segmentation map through jump connection and calculating a fitness vector; the optimization scheduling module is used for obtaining an optimized submarine landform semantic segmentation map; and the post-processing and output module is used for generating a final segmentation result and obtaining a landform category identification result. According to the method, the problems of fuzziness and fracture of a traditional convolutional neural network on a sea ditch-sea mountain-hill complex nested landform boundary are effectively solved.

The invention discloses a visual large model PCB defect detection system based on a YOLOv10 deep learning network. The system comprises an image screening module, a defect area positioning module, a defect category identification module, a defect data integration module and a defect evaluation display module. According to the method, the image of the printed circuit board is acquired through an industrial camera, an image quality evaluation standard is set, a high-quality input image is screened, the influence of interference factors is suppressed, image features are captured by a YOLOv10 network trunk extraction layer, a positioning area is accurately screened by applying the intersection and combination ratio of a prediction frame and a real frame, and the offset between the center of a candidate frame and the position of an anchor frame is calculated. According to the method, the positioning precision is strictly controlled, the false detection rate is effectively reduced, defect area images are cut and classified through YOLOv10 classification branches, and detection results are uniformly mapped into standard defect type identifiers according to informatization processing, so that the uniformity and standardization of detection types are realized, and the PCB defect detection efficiency is effectively improved.

The invention discloses a snapshot type hyperspectral imaging industrial online sorting system which comprises a support assembly, an optical imaging assembly and a motion control assembly. The bracket assembly consists of a bottom plate, and an adjustable bracket and a fixed bracket which are fixed on the bottom plate; the optical imaging assembly comprises a lighting unit, an imaging objective lens and a snapshot type hyperspectral camera, and can synchronously obtain space image information of a measured object and spectral information of a continuous wave band in a single exposure process; the motion control assembly realizes automatic transmission of the measured object through the transmission platform; the data processing module is integrated with an image preprocessing unit, a spectral analysis unit and a spectral database, and is used for end-to-end closed-loop control from original hyperspectral data generation to sorting decision making; the system adopts a metasurface light splitting structure to realize real-time decoding, performs material or category identification by using a pre-trained classification model, and finally automatically generates a sorting decision signal according to an identification result to drive a motion control assembly to complete sorting operation.

The invention discloses an event detection and extraction method and system based on entity and trigger word fusion recognition. The method comprises the steps that trigger words in event text data are extracted; mapping the event text to a vector space based on a word embedding model, and constructing an event text classifier to perform similarity measurement on all words of which the part-of-speech is a verb and the trigger word in the event text; constructing an event detection model to obtain an event category in the event text data; identifying a key element entity of the event; constructing a dependency syntax tree to represent a syntactic dependency relationship of the sentences; obtaining the score of the dependency syntax tree; and inputting the preprocessed word vector and the part-of-speech vector representation obtained through supervised learning into a role tagging model for fusion to obtain a vector representation with part-of-speech features, and after training, carrying out conditional random field processing to obtain a final semantic role tag of the word. Accurate event detection based on entities is realized, and high-quality extraction of events is towed.

The invention provides a hyperspectral image open set classification method based on multi-scale convolution and feature fusion, and aims to improve the classification precision of a known category and an unknown category in a hyperspectral image and the robustness of a classification model. According to the method, spectral and spatial features are fused through a deep learning model, and the problems of unknown category identification and feature optimization in hyperspectral image classification are solved. Firstly, spectral features are extracted through multi-scale convolution, and in combination with a channel attention mechanism, capture of most discriminative spectral information is enhanced. And secondly, through a multi-branch structure and a convolution enhancement technology, the expression of spectral features is optimized, and the perception capability of complex spatial information is improved. And the spatial features are enhanced through a high-frequency enhancement technology, so that the sensitivity to detail and edge information is improved. Through the innovative feature extraction and fusion strategy, the classification precision of the hyperspectral image open set and the robustness of the classification model are improved.

The invention provides a WSI long-tail data identification method and device based on multi-modal distillation guidance and a readable storage medium thereof, and aims to solve the problem of low tail category identification accuracy caused by long-tail distribution in weak supervision multi-instance learning of a digital pathological total image (WSI). According to the method, performance improvement is realized through double-branch integrated multi-instance learning, text feature extraction and prompt generation and multi-modal distillation optimization. The double branches are trained by adopting balanced distribution and original long-tail distribution data respectively, an aggregator is shared, and prediction consistency is constrained; text features containing semantic prompts are generated to serve as supervision signals, and semantic alignment is achieved through image-text comparison loss; soft and hard label collaborative distillation loss is designed, and a total loss function is constructed in combination with consistency loss. Experiments show that the algorithm can significantly improve the tail category and overall recognition performance, is high in universality, can adapt to various multi-instance learning methods, and provides reliable technical support for intelligent analysis of pathological images.

The invention provides a data classification and grading method and system, and belongs to the technical field of data processing, and the method comprises the steps: obtaining the class information of to-be-processed data, and determining the grading rule information according to the class information; inputting the to-be-processed data and the grading rule information into a target cue word template to generate a target cue word; the target prompt word template is determined according to the format of the to-be-processed data; inputting the target cue word into a target grading model to obtain grading information output by the target grading model; wherein the target grading model is obtained by matching from a pre-constructed model library based on the format of the processing data. According to the method, the accuracy of data classification and grading is effectively improved by combining dynamic category recognition, target cue word generation and grading model dynamic matching based on the model library.

The invention discloses a core rock sampling automatic identification system and method based on image processing and machine learning. The system comprises a drilling assembly, a sensor assembly, a measurement and control lamp assembly, an identification assembly, a relay assembly and a control and display assembly. The method comprises the steps that core rock drilling is conducted, and a core rock sample is obtained through extraction; core rock sample images are collected, and parameters in the drilling process are monitored; sorting physical and appearance characteristics of the obtained core rock sample and the core rock sample image, and storing the physical and appearance characteristics in a database; preprocessing the acquired core rock sample image, and extracting key features; training by using a machine learning algorithm to obtain a core rock category recognition model; and applying the core rock category identification model obtained by training to newly obtained core rock sample image data to obtain classification and identification results. According to the method, the sensor, image processing and the convolutional neural network are combined, automatic and real-time rock core recognition is realized, the recognition efficiency and accuracy are improved, and the method can be flexibly applied to a field environment.

The embodiment of the invention belongs to the technical field of image processing of artificial intelligence, and relates to a micro-plastic type identification method and device, computer equipment and a storage medium, and the method comprises the steps: receiving a to-be-classified fluorescence spectrum image sent by a user terminal; performing preprocessing operation on the fluorescence spectrum image to obtain a preprocessed spectrum image; the trained spectrum classification model is called to carry out category identification operation, a category identification result is obtained, and the spectrum classification model adopts a random forest and a support vector machine method to carry out classification; and outputting the type identification result to the user terminal. According to the method, rapid identification and classification of the micro-plastics can be realized, the input image is the spectral image acquired by using the microspectrometer, the visual prediction result is output by the machine learning model after spectrum extraction, and the identification accuracy of the micro-plastic spectrum is effectively improved.

The invention relates to a rapid three-dimensional construction and recognition method for a complex scene, belongs to the technical field of intelligent mobile robots or unmanned vehicles, and solves the technical problems of low three-dimensional construction efficiency and low semantic recognition accuracy of the complex scene. Comprising the following steps: acquiring a point cloud registration image and an RGB-D image of a complex scene; performing multi-scale feature extraction based on the RGB-D image to obtain multi-scale image features; obtaining multi-scale scene point cloud features of the point cloud registration graph; performing multi-level feature fusion on the multi-scale image features and the multi-scale scene point cloud features to obtain a multi-scale fusion feature vector, and performing voxelization representation based on the multi-scale fusion feature vector to obtain a voxelization three-dimensional scene; and reasoning the voxelized three-dimensional scene to obtain a semantic recognition result corresponding to the complex scene. And rapid three-dimensional scene construction and category identification of a complex scene are realized.

The invention relates to an instance segmentation network optimization method and device and a medium, and the method comprises the steps: obtaining a batch of tail data samples, and carrying out the data augmentation of the tail data samples, and obtaining a tail extension sample; performing comparative learning on the tail data sample and the tail expansion sample to obtain tail comparative loss; constructing a pseudo semantic tag of the tail extension sample; acquiring a batch of head data samples, wherein the head data samples and the tail data samples both comprise truth value labels; inputting the head data sample, the tail data sample and the tail expansion sample into a preset instance segmentation network, and calculating segmentation mask loss and category loss based on a truth value tag and a pseudo semantic tag; and adjusting a preset instance segmentation network based on the tail comparison loss, the segmentation mask loss and the category loss to obtain an optimized instance segmentation network. The problem that the long-tail instance segmentation effect is poor due to the fact that an existing model is low in tail rare category recognition accuracy is solved.

The invention discloses a communication interference signal open set identification method, system and device and a medium. The method comprises the following steps: carrying out short-time Fourier transform on a communication interference signal to obtain a time-frequency graph; performing multi-scale feature representation on the time-frequency diagram by using a Res2Net-based multi-scale feature extraction network; constructing a projection layer to carry out hyperspherical embedding on the multi-scale features, carrying out modeling by utilizing vMF distribution, and designing a compactness and dispersity loss function; multi-scale features after modeling are input into an OpenMax layer for open set recognition, output of a projection layer is used as an activation vector to improve OpenMax, Euclidean distance is replaced with cosine distance, input sample unknown is calculated based on an extreme value theory, category probability distribution is adjusted, and some abnormal samples can be classified into unknown categories; the Res2Net more flexibly captures multi-scale information than the convolutional network; the separation of inter-class features and the compactness of intra-class features are effectively enhanced through the hyperspherical vMF distribution; the OpenMax layer endows the network with unknown category identification capability; the method has better performance and wider universality; the system, the equipment and the medium are used for realizing the communication interference signal open set identification method.

The invention belongs to the technical field of information data processing, and particularly relates to a method and device for solving catastrophic memory of learning forgetting based on knowledge distillation. The method comprises the steps that an initial model is selected, a standard data set is used for training, and initial model parameters are optimized by minimizing cross entropy loss; selecting a target forgetting category, adjusting model parameters by applying a category forgetting method, and generating a forgetting model which is low in target forgetting category identification capability and high in other category identification accuracy; and selecting residual category data from the original data set as incremental learning data, adding quantitative target forgetting category data as input data, taking the generated forgetting model as a teacher model, initializing a student model, constraining output distribution by the student model on the input data through knowledge distillation loss, and updating parameters of the student model. The problem that the forgotten model cannot keep the forgetting performance in the incremental learning process, and catastrophic forgetting may occur when new information is absorbed is solved.

The invention provides a fruit and vegetable identification and identification model and optical element combined training method and device, and relates to the artificial intelligence fields of deep learning, computer vision, image processing, micro-nano optical design and the like. The fruit and vegetable recognition method comprises the steps that a to-be-processed target image is obtained, the target image is a to-be-recognized fruit and vegetable image generated by a photosensitive element according to an obtained second optical signal, and the second optical signal is an optical signal obtained after an optical element modulates an obtained first optical signal, the first optical signal is an optical signal that light emitted by the light source irradiates to-be-identified fruits and vegetables and then enters the optical element through reflection; and generating a recognition result corresponding to the target image by using the recognition model, wherein the recognition result comprises a category recognition result and a freshness recognition result of the to-be-recognized fruits and vegetables.

The invention relates to the technical field of computer vision and deep learning, in particular to a fresh cut flower automatic classification method and system based on a two-step recognition neural network. According to the method, an image is collected, the background of the image is preprocessed, and meanwhile a flower main body area is reserved; feature extraction is carried out on the processed image, and the fresh cut flower category is identified; processing the image, and cutting the image to a flower core area; and finally, performing feature optimization on the cut image, extracting fine-grained features, and performing grade classification on the fresh cut flowers according to the fine-grained features. Based on parameter sharing and a two-step identification strategy, classification precision and training efficiency are improved through a hierarchical architecture of'large class identification-grade subdivision ', so that fine-grained features are rapidly extracted, and accurate grade classification is performed on the fresh cut flowers according to the fine-grained features.

The embodiment of the invention provides an image processing method and related equipment, and the method comprises the steps: obtaining a first sample image which comprises one or more objects; calling a pseudo tag generation model to perform feature extraction processing on the first sample image to obtain a first feature map; the pseudo label generation model is obtained by training a second sample image, and the image styles of the second sample image and the first sample image are different; performing domain generalization processing on the first feature map through a pseudo label generation model to obtain a domain generalization first feature map; the style information contained in the first feature map of the domain generalization is less than the style information contained in the first feature map; and through a pseudo label generation model, performing category identification processing according to the first feature map of the domain generalization to obtain a pseudo label of the first sample image, wherein the pseudo label is used for indicating the category to which the object in the first sample image belongs. According to the embodiment of the invention, the generation quality of the false label of the image can be improved.

The invention belongs to the technical field of industrial visual inspection, and provides an obstacle detection model training method and an obstacle detection method based on comparative learning and feature fusion. The training method comprises the steps of obtaining a point cloud sample set; extracting statistical and geometric feature vectors of each point cloud sample; pre-training the multi-scale feature fusion network by using the point cloud sample set based on comparative learning; constructing an obstacle detection network, wherein the obstacle detection network comprises a pre-trained multi-scale feature fusion network and a classifier; training an obstacle detection network by using the point cloud sample set to obtain an obstacle detection model; the multi-scale feature fusion network comprises M cascaded feature extraction modules; the cross-layer splicing module is used for splicing the statistical and geometric feature vectors of the point cloud samples and M feature matrixes; and a channel attention module and a point cloud feature projection head. According to the method, the precision of 3D point cloud obstacle category identification and the generalization ability of the classifier can be improved, and the model training convergence speed is high.

The invention discloses a ship category identification method based on incremental learning, and the method comprises the steps: carrying out the improvement of a training mechanism and a learning strategy of an original YOLO11 model, inputting a training set 1 into the original YOLO11 model for training, and obtaining the optimal weight of the model; the optimal weight of the model is loaded to the improved YOLO11 model, and an incremental target detection model is constructed through an EWC algorithm and a periodic data playback strategy; training an incremental target detection model by using the training set 2, and introducing a differential regularization strategy to enable the model to obtain the ability of inhibiting disastrous forgetting; and testing the incremental target detection model by using the test set, and continuously adjusting the regularization strength to obtain an optimal detection result. Incremental learning and target detection are combined, new ship categories are continuously learned through the model, meanwhile, stable detection performance of original ship categories is kept, and the problem of knowledge forgetting in the model iteration process is effectively solved.

The embodiment of the invention provides a model training method and device and a user category identification method and device. The method comprises the following steps: screening out a plurality of target samples from a plurality of user data samples by utilizing a preset sampling algorithm, and combining the plurality of target samples and category labels corresponding to the plurality of target samples to obtain a target sample set; performing feature extraction on the plurality of target samples to obtain target features, and identifying and aggregating the target features by using a preset feature processing algorithm to obtain a target feature set; inputting the target feature sets corresponding to the plurality of target samples into a to-be-trained recognition model to obtain a category prediction result; and for each target sample, calculating a target gradient value according to the category prediction result and the category label, and adjusting model parameters of the recognition model based on the target gradient value. According to the scheme, through combination of sample dimensionality reduction and feature dimensionality reduction, the calculation complexity can be effectively reduced, and the efficiency of data processing and model training is improved.

The invention discloses an image detection network training method and an image detection method, system and device, and relates to the technical field of image detection. The method comprises the steps of obtaining a plurality of image data sets and weights corresponding to the image data sets, and obtaining a to-be-trained image detection network according to the image data sets; sampling the image data set according to the weight to obtain a sampled image; detecting the sampling image by using the to-be-trained image detection network to obtain a first category recognition result and a first region recognition result of a detection object in the sampling image; obtaining a first loss value according to the first region identification result, and obtaining a second loss value according to the first category identification result; and adjusting network parameters of the to-be-trained image detection network according to the first loss value and the second loss value, adjusting the weight according to the second loss value, and returning to the step of sampling the image data set according to the weight until the number of times of adjusting the network parameters reaches a first preset number of times, thereby obtaining a trained image detection network.

The invention discloses a battery pack consistency evaluation method and device and electronic equipment, and belongs to the technical field of lithium batteries, and the method comprises the steps: extracting a plurality of voltage characteristics of a charging and discharging voltage change curve segment of a single cell in a battery pack, and a plurality of temperature characteristics of a temperature change curve segment; adding category labels for the curve segments according to the performance of the single battery cells; training a neural network model according to the voltage change curve segments, the voltage features, the temperature change curve segments, the temperature features and the category labels to obtain a category recognition model; and identifying the category of each single cell in a target battery pack based on the category identification model to obtain the category of the single cell, and evaluating the consistency of the target battery pack according to the category of the single cell. According to the invention, through feature extraction and analysis of the voltage change curve and the temperature change curve, the type of each battery monomer is determined, and rapid and accurate battery pack consistency evaluation is realized.

The invention relates to an input image category identification method and device, computer equipment and a storage medium, and the method comprises the following steps: carrying out the preprocessing of an input image, and obtaining a standard input image; extracting character information from the standard input image based on a pre-trained OCR model in combination with a polygon fitting technology; performing preliminary classification according to keywords, themes and context information of the text information to obtain a preliminary classification result; performing feature fusion on the visual features, the text features and the preliminary classification result of the standard input image by adopting a hierarchical feature fusion method to obtain image fusion features; and performing category division on the standard input image according to the image fusion feature and a pre-stored image template feature to obtain an input image category. The method and the device can be applied to financial service system application scenes, and can effectively realize accurate category identification of input images in a financial service system so as to facilitate subsequent system image information input.

The invention provides a power transmission line icing detection method and system, and relates to the technical field of intelligent control, and the method comprises the steps: eliminating the transmission interference of an optimization processing signal through a Manchester encoding protocol and a local cache verification algorithm in a channel switching process, and obtaining a clean transmission signal; and inputting the clean transmission signal into a pre-trained deep convolutional neural network, extracting ice layer space attributes through a plurality of convolutional layers and pooling layers, performing feature fusion through a full connection layer, calculating ice layer thickness distribution, identifying ice layer categories through a softmax classifier, and identifying the ice layer categories based on a thickness distribution diagram and an ice layer category identification result. And generating a detection result containing the ice layer thickness value and the spatial distribution attribute. According to the method, regional accurate analysis can be realized, and local feature omission caused by traditional overall analysis is avoided.

This disclosure provides systems and methods for controlling a vehicle based on a combination of high precision detection and high recall detection. The disclosed systems and methods can efficiently generate trajectories by reducing duplicate detection or duplicate calculation of objects or obstacles of common and known object types and objects or obstacles without class identification.

The invention relates to the technical field of emotion analysis, and provides a psychological catharsis interaction system based on artificial intelligence, which comprises a data acquisition module, an emotion recognition module, a virtual catharsis scene module and an emotion analysis and guidance module, physiological data and behavior data of students are collected through VR equipment and a sensor, an emotion recognition model is constructed in combination with feature extraction and a multi-modal feature fusion technology, and emotion category recognition is achieved; dynamically generating an adaptive virtual catharsis scene based on an identification result; the emotion analysis and guidance module optimizes hyper-parameters of a TCN-GRU model through an improved grey wolf optimization algorithm, constructs an enhanced TCN-GRU model, extracts long-term and short-term features from virtual catharsis interaction data, analyzes emotion evolution and provides personalized psychological guidance; the system can generate an accurate emotion evolution map and psychological state scores, and provides a scientific and accurate psychological intervention scheme for students.

The invention discloses a track fusion method for incremental updating of a Gaussian mixture model and multi-state transition filtering, and relates to the technical field of track fusion, and the method comprises the steps: carrying out the preprocessing of observation information of a radar and electronic reconnaissance equipment; obtaining a feature vector of the observation information and performing target category identification to obtain a target category label set; generating an initial track of the observation subset by using a multi-correlation gate and a cost function; identifying track pairs with potential interaction and / or conflicts from the initial track set to form a target track set without intersection and / or interference; based on a plurality of preset state transition models, using the track quality score set and the sensor weight set to obtain a track fusion result; on the premise of considering the real-time performance and the precision, the cross track and the high maneuvering target are efficiently processed based on the range and performance difference of multiple sensors, and key filtering and associated parameters are adaptively adjusted according to the change of the environment and the sensor state, so that the continuous and accurate tracking of the target track information is realized.