A millimeter wave security door target recognition model training method

By extracting features and transforming dimensions from millimeter-wave security gate echo data, and using the Softmax function to calculate predicted probabilities and update model parameters, the accuracy of target recognition is improved, solving the problem that high-dimensional features are difficult to capture fine-grained features.

CN122174034APending Publication Date: 2026-06-09BEIJING HUAHANG RADIO MEASUREMENT & RES INST
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING HUAHANG RADIO MEASUREMENT & RES INST
Filing Date
2024-12-30
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

In millimeter-wave security gate echo target recognition tasks, high-dimensional features are difficult to capture the fine-grained characteristics of the target, resulting in low target classification and recognition accuracy.

Method used

By extracting features and transforming dimensions from batches of raw millimeter-wave security gate echo data, feature maps for each target category are obtained. Vector aggregation is then performed on the feature maps for each target category, and the prediction probability is calculated using the Softmax function to update the model parameters until the training termination condition is met.

Benefits of technology

It improves the accuracy of target recognition and classification, fully extracts the characteristics of high-dimensional features, and solves the problem that high-dimensional features are difficult to capture fine-grained features of targets.

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Abstract

The application discloses a millimeter wave security door target recognition model training method, belongs to the technical field of millimeter wave security door target recognition, and solves the problem of low target classification and recognition accuracy caused by the difficulty of capturing the fine-grained characteristics of targets by high-dimensional data characteristics. The method comprises the following steps: a millimeter wave security door target recognition model extracts features and converts dimensions from batch original millimeter wave security door echo data, obtains a feature map of each target category, and obtains a total score of each target category after vector aggregation; the total scores of all target categories are input into a Softmax function, and the prediction probability of each target category is calculated; a loss function is calculated according to the prediction probability output by the Softmax function and the real target category label, and the parameters of the millimeter wave security door target recognition model are updated; if the training end condition of the millimeter wave security door target recognition model is reached, a millimeter wave security door target recognition model that passes the training is obtained.
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