Banknote anomaly detection method and system based on big data

By applying artificial intelligence and deep learning technology in banknote abnormal detection, the tone and content semantic features of the photovariable ink area are extracted, and the detection is performed using significant sparse interactive fusion representations, which solves the impact of lighting conditions and image quality on detection accuracy in the prior art and improves detection accuracy.

CN119049170BActive Publication Date: 2025-03-21RIZHAO FINANCE ROADSHOW CO LTD
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Patent Information

Application Number
CN202411524063.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-10-30
Publication Date
2025-03-21
Estimated Expiration
2044-10-30

AI Technical Summary

Technical Problem

The existing banknote abnormality detection method for photovariable ink areas has problems with different image acquisition differences and high image quality requirements under different lighting conditions, resulting in low detection accuracy.

Method used

Image processing and analysis algorithms based on artificial intelligence and deep learning technology are used to extract tones and content semantic features of the optically variable ink area, and detect whether the banknote is abnormal through significantly sparse interactive fusion representations.

Benefits of technology

By capturing various characteristics of banknotes, avoiding the influence of ambient lighting conditions, reducing the impact of image noise and processing process errors, thereby improving the accuracy of banknote abnormality detection.

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Abstract

The present application provides a banknote anomaly detection method and system based on big data, which relates to the field of intelligent detection. It extracts hue features and content semantic features of optically variable ink areas by applying image processing and analysis algorithms based on artificial intelligence and deep learning technology, so as to depict the hue and content semantic feature information of the optically variable ink area, and detects whether the banknote is an abnormal banknote based on the significant sparse interactive fusion representation between the two hierarchical features of the optically variable ink area. In this way, it is possible to capture multiple features of the banknote more comprehensively, avoid the influence of ambient lighting conditions when performing banknote anomaly detection, reduce the influence of image noise and processing errors, thereby more comprehensively identifying abnormal banknotes and improving the accuracy of detecting whether the banknote to be detected is an abnormal banknote.
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