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DATA DETECTION METHOD AND APPARATUS, COMPUTER, STORAGE MEDIUM, AND PROGRAM PRODUCT

InventionPending
272views
20Claims · 3 independent
§ Ⅰ

Dossier Overview

Inventor

Changan WANG

IPC Classification

G6V 10/77G6V 10/74G6V 10/82

CPC Classification

G6V10/7715G6V10/761G6V10/82

A data detection method includes obtaining an image sample, extracting sample features from the image sample, determining a target sample feature with a maximum nearest-neighbor feature distance, determining, based on the nearest-neighbor feature distances of the sample features, (N+1) sample feature categories corresponding to the sample features, N being a positive integer, and training an initial feature classification model based on the (N+1) sample feature categories and the sample features to obtain a trained feature classification model. The nearest-neighbor feature distance of one sample feature is a minimum value of feature distances between the one sample feature and other ones of the plurality of sample features except the one sample feature. The (N+1) sample feature categories include N first feature categories indicating detected data being normal data and a second feature category indicating to perform secondary detection on the detected data.

Original (Chinese)

A data detection method includes obtaining an image sample, extracting sample features from the image sample, determining a target sample feature with a maximum nearest-neighbor feature distance, determining, based on the nearest-neighbor feature distances of the sample features, (N+1) sample feature categories corresponding to the sample features, N being a positive integer, and training an initial feature classification model based on the (N+1) sample feature categories and the sample features to obtain a trained feature classification model. The nearest-neighbor feature distance of one sample feature is a minimum value of feature distances between the one sample feature and other ones of the plurality of sample features except the one sample feature. The (N+1) sample feature categories include N first feature categories indicating detected data being normal data and a second feature category indicating to perform secondary detection on the detected data.

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