Certificate bill positioning detection method based on numerical prediction regression model

Abstract

The invention relates to a certificate bill positioning detection method based on a numerical prediction regression model. The method comprises the following steps: (1) obtaining a training sample; (2) constructing a numerical prediction regression model, wherein the numerical prediction regression model comprises a lightweight neural network and a spatial transformation network which are connected in series, the input of the lightweight neural network is a to-be-positioned image, the output of the lightweight neural network is a feature convolution graph, the input of the spatial transformation network is the feature convolution graph, and the output of the spatial transformation network is coordinates of four key points of a certificate bill in the to-be-detected image; (3) designing a loss function; (4) training a numerical prediction regression model by using the training sample in the step (1); (5) inputting a to-be-positioned image into the trained numerical prediction regressionmodel, and obtaining coordinates of four key points of the certificate bill in the to-be-detected image; and (6) selecting a certificate bill image according to the coordinate circles of the four keypoints of the certificate bill. Compared with the prior art, the method is accurate and reliable in result.

Description

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AI Technical Summary

Benefits of technology

This technology helps identify certificates or bills more accurately by learning their location from its marked dataset without containing any irrelevant parts like other things that may be mistaken for them. It also allows us to quickly train models while reducing storage requirements. Overall, this innovation improves efficiency and accuracy over existing methods.

Problems solved by technology

The technical problem addressed in this patents relates to improving the accuracy and efficiency of identifying documents/bill patterns during machine vision systems due to factors like light conditions, shading effects, and variations between color spaces caused by varying environmental elements. Existing techniques have limitations including slow calculations speed, lack reliability, require excess human effort, and may be affected by irrelevant data.

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