7 results about "Age classification" patented technology

The invention relates to a lightweight millimeter-wave radar children age classification method based on feature engineering. The method comprises the following steps: acquiring a millimeter-wave radar vital sign data set containing a plurality of children of different ages; extracting a respiratory signal; calculating a plurality of candidate features from the respiratory signal, constructing a candidate feature set, and screening out a key feature subset for children age classification from the candidate feature set; training the lightweight machine learning model to obtain a child age classification model; and processing the input millimeter wave radar signal, and outputting a child age classification result. The method has the beneficial effects that the calculation complexity and the storage overhead of the model are remarkably reduced, so that the high-precision child age classification model can efficiently run on a resource-limited embedded platform.

The invention discloses an intelligent tea age grading method and device based on deep learning, and relates to the technical field of tea grading, and the method comprises the steps: S1, obtaining a tea image; s2, constructing a lightweight tea classification network; wherein the tea texture sensing module performs parallel extraction by using a parallel multi-scale convolution kernel; the channel attention module performs feature enhancement by using a channel attention mechanism; the multi-dimensional feature extraction module performs feature extraction by using four parallel branches to obtain color features, texture features, morphological features and time sequence features, and fuses the color features, the texture features, the morphological features and the time sequence features; the classification output module outputs the tea age group category of the tea image; s3, training the lightweight tea classification network; and S4, performing age classification evaluation on the tea image by using the trained lightweight tea classification network. According to the method, the tea texture sensing module and the channel attention module are introduced, and time sequence analysis is introduced, so that the method has better recognition precision and adaptability in a tea recognition task.

The application discloses an age classification model generation method, an age classification method and system, and a training method. i Input into a face feature extraction network to obtain a face feature map f i ; the face feature map f i is input into a feature discrimination module D f for first optimization processing; the face feature map corresponding feature vector is input into a first full connection layer for second optimization processing; and iterative training is performed to obtain an age classification model. In terms of training data, the weight setting of data sampling is performed on face images of different age stages, and the data proportion of different age stages is balanced; in model optimization, a feature discrimination module and a ViT module are introduced to reduce the ambiguity information of face images of different age stages, and accurate and stable prediction is realized.

The present application relates to the technical field of image processing, in particular to a kind of recycling material quality automatic identification and classification method based on image processing, by detecting old household appliance panel lap joint boundary, extract the mean difference of yellow and blue channel of exposure side and shielding side, construct net yellowing amount difference signal, and take shielding side color as original color reference datum;Combined with the anisotropic characteristics of texture, ABS main body and PP ventilation structure area segmentation is realized, the net yellowing amount of each material and the partition confidence are obtained;Further calculate the cross-material chroma shift direction angle difference to construct degradation path characteristics, form two-dimensional feature vector with net yellowing amount and map to pre-built age characteristic space to complete manufacturing age classification;Through the sufficiency of data determined by coverage index, when the condition is met, output deterministic classification result, otherwise output interval age range and retake guide information.The method improves the accuracy and reliability of identification and classification in multi-material mixed scene.

This invention discloses a method and system for assessing the insulation aging status of high-voltage equipment, relating to the field of high-voltage equipment insulation detection technology. The invention first extracts original image data of high-voltage equipment with different insulation states, aging types, and degrees from historical insulation aging records, constructs a standard sample library, and preprocesses it to obtain standard image features. Then, it collects image features of the equipment to be assessed, compares them with the standard samples to calculate the comprehensive similarity, determines the insulation state, and identifies the aging type. Next, it extracts operating and environmental parameters, analyzes their influence on the target aging type, and calculates a standard influence benchmark value. Then, it combines the similarity and the two benchmark values ​​to comprehensively assess the insulation aging degree, finally classifying the aging level and triggering the corresponding early warning mechanism. This invention, through the fusion assessment of image features and operating environmental parameters, can identify insulation aging types and calculate the aging degree, achieving high-voltage equipment insulation aging classification and accurate early warning.

The application relates to a ginseng age nondestructive classification model construction method and a ginseng age nondestructive classification system, in particular to a ginseng growth age classification method based on hyperspectral imaging technology and an improved swarm intelligence optimization algorithm. The method solves the problems of high-dimensional data redundancy, insufficient utilization of atlas features, insufficient model generalization ability and low precision of fine classification of adjacent cultivation ginseng ages in the prior art. The method comprises the following steps: 1. acquiring hyperspectral image data of different ginseng ages and performing pretreatment; 2. screening spectral features and texture features based on the hyperspectral image data; 3. performing feature-level fusion on the spectral features and the texture features to obtain fused features; and 4. inputting the fused features into an IHSO-RF model to perform training and testing. The classification model is embedded in the system to realize the classification of ginseng ages. The application is suitable for intelligent perception and analysis based on hyperspectral imaging and machine vision, nondestructive detection and grading of agricultural products / Chinese herbal medicine quality and the like.

This application relates to the field of image recognition technology, and in particular to an age recognition model training method, recognition method, device, equipment, and medium. The method constructs a first age classification model and a second age classification model. The second age classification model has the same architecture as the first age classification model, and the parameters in the updated second age classification model are calculated based on the parameters of the updated first age classification model. The second age classification model is used to construct pseudo-labels for unlabeled data. The age classification model is trained using both unlabeled and labeled data, and the model parameters are updated using age labels and pseudo-labels identified by the model for unlabeled data, respectively. This effectively improves the model's age classification accuracy through self-supervised learning by accurately constructing pseudo-labels.