Banknote Detector Pixel Comparison Ink-Dye Detection
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Solution Overview
Problem
Conventional banknote sorting and counting devices face challenges in detecting ink-dyed banknotes effectively, which are a result of unauthorized opening or robbery, leading to security issues and increased operational costs in cash handling.
Innovation Solution
A method and device that aligns and classifies banknote images, positions printed patterns, and compares them pixel-by-pixel with reference images to accurately detect ink-dyed banknotes, using infrared and RGB image sensors, and a processing unit to determine authenticity and orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional banknote sorting and counting devices use full image scanning in visible and infrared spectral ranges, then authenticity and decay level determination is improved, but detection capability for ink-dyed banknotes remains insufficient
Solution Approach 1:
The patent divides the banknote image into multiple regions of interest (ROIs) corresponding to specific printed patterns such as water marks, security features, and denominational markings. Instead of analyzing the entire image, the system selectively processes only these critical regions, reducing computational complexity while maintaining high detection accuracy for ink-dyed alterations in patterned areas.
Solution Approach 2:
The invention applies different analysis methods to different regions of the banknote image based on their specific characteristics. Patterned regions with security features receive enhanced analysis using pattern recognition algorithms, while uniform regions are processed with simpler techniques. This localized approach optimizes detection capability for ink-dyed patterns without unnecessarily processing the entire image.
2Measurement precision
If banknote images are aligned and pattern-positioned with reference images, then detection precision for ink-dyed areas is improved, but processing time increases
Solution Approach 1:
The patent performs alignment and pattern positioning operations in advance during the image acquisition phase. Reference banknote images are pre-processed and stored with their corresponding pattern locations. When a new banknote is scanned, the system quickly matches it against the pre-prepared reference data, reducing real-time processing requirements and enabling faster detection while maintaining high precision.
Solution Approach 2:
The system creates a digital copy of the reference banknote image and its pattern locations for comparison purposes. Instead of physically manipulating or re-scanning the reference banknote, the pre-stored reference image serves as a template for rapid pattern matching and alignment, significantly reducing processing time while maintaining measurement precision.
3Measurement precision
If pixel-by-pixel comparison is performed on aligned banknote images, then detection accuracy for ink-dyed banknotes is improved, but computational load increases
Solution Approach 1:
The patent segments the pixel-by-pixel comparison process into regional analyses focused on specific printed patterns and security features. Instead of comparing every pixel across the entire banknote image, the system concentrates computational resources on critical pattern regions where ink-dyed alterations are most likely to occur, reducing overall computational load while maintaining high detection accuracy.
Solution Approach 2:
The invention dynamically adjusts comparison parameters based on the detected banknote characteristics and risk assessment. For banknotes with high suspected alteration risk, the system applies more stringent comparison thresholds and enhanced analysis to specific regions. For lower-risk banknotes, it uses faster, less computationally intensive comparison methods, optimizing the balance between detection accuracy and computational power consumption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the detection capabilities of ink-dyed banknotes, improving security and reducing operational costs by accurately identifying and sorting out counterfeit or altered banknotes within standard ATMs or banknote handling machines.
Implementation Method 1
using full images—obtained with scanning devices—of both banknote sides inter alia in the visible spectral range and in the infrared spectral range
Implementation Method 2
the BI and RBI, being in exact pattern position in relation to each other, are compared pixel per pixel according to a predefined comparison procedure
Data Source
AI summary
A banknote detector device for an automatic teller machine, for differentiating between non-accepted and accepted banknotes, includes a banknote image sensor to receive and scan at least one face of an input banknote and to store a banknote image (BI) of each scanned. The image includes image data in the form of a number of pixels; and a reference banknote image (RBI) storage where one reference banknote image, being processed from a predetermined number of banknote images from accepted banknotes, is stored for each face of each banknote. The device includes an alignment, a banknote face classification unit, a printed pattern positioning unit and a comparison unit where, for at least one face of the banknote, the BI and RBI, being in exact pattern position in relation to each other, are compared pixel per pixel according to a predefined comparison procedure to classify the banknote as accepted or non-accepted.