Currency detecting method of currency detector based on edge positioning

Abstract

The invention discloses a currency detection method of a currency detector based on edge positioning, relates to the technical field of currency detection, and solves the technical problems that systematic analysis processes for different detection results are lacked, authenticity of banknotes cannot be comprehensively evaluated, and the reliability and stability of currency detection are insufficient in the face of complex and diversified counterfeit means. According to the invention, ultraviolet fluorescence detection and infrared induction detection are carried out in sequence, and systematic analysis of detection results is combined, so that authenticity of banknotes can be judged more comprehensively and accurately, in infrared induction detection, infrared edge features are analyzed from two aspects of stability and regularity, currency detection accuracy is improved, misjudgment and missed judgment rates are reduced, and the detection accuracy is improved for abnormal comparison results. The relative position relation is judged by calculating the distance between the abnormal edge feature and the normal edge feature, the texture feature of the abnormal edge is further deeply analyzed, the finely-forged counterfeit money is effectively recognized, and the coping capacity of the money detection technology to complex counterfeit means is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of banknote detection, and specifically to a banknote verification method for a banknote detector based on edge positioning. Background Art

[0002] A banknote detector is a machine that verifies the authenticity of banknotes and counts the number of banknotes. Due to the large scale of cash circulation and the heavy workload of cash handling at bank teller counters, a banknote counting machine has become an indispensable device. A banknote detector is mainly used for counting cash, identifying counterfeits, and sorting banknotes, and is widely used in various financial industries and various enterprises and institutions with cash flow.

[0003] According to the patent application with the publication number CN118470849A, a banknote verification method and system for a banknote detector based on image processing are disclosed. Among them, the banknote verification method based on image processing includes steps such as preliminary identification, establishing a scanning area, image acquisition, image processing, mapping, feature point extraction, inclination calculation, primary correction calculation, secondary identification, verification passed, and verification failed; the banknote verification system includes an identification module, a scanning module, an acquisition module I, a processing module, a mapping module, an acquisition module II, a calculation module I, a calculation module II, an identification module II, and an output module.

[0004] Traditional banknote verification methods mainly rely on manual experience and simple physical feature recognition, such as observing watermarks, security threads, fluorescent patterns, etc. However, with the continuous upgrading of counterfeiting technology, these methods are difficult to meet the requirements of accurate counterfeit identification. Currently, image recognition technology is gradually applied in the field of banknote verification, using technologies such as edge detection and multispectral analysis to improve the accuracy of banknote verification. However, the existing technology still has deficiencies in comprehensively and accurately analyzing by integrating multiple detection means, and it is difficult to effectively identify complex counterfeiting means. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides a banknote verification method for a banknote detector based on edge positioning, which solves the problems of lacking a systematic analysis process for different detection results, being unable to comprehensively evaluate the authenticity of banknotes, and having insufficient reliability and stability of banknote verification when facing complex and diverse counterfeiting means.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A banknote verification method for a banknote detector based on edge positioning, which specifically includes the following steps: Use the Canny algorithm to perform edge detection on the acquired banknote image, extract edge features, and the edge features obtained here include characters, patterns, shapes, and lines; Perform ultraviolet fluorescence detection on the extracted edge features, compare the obtained fluorescent edge features with the comparison features of genuine banknotes, and generate an abnormal or normal comparison result; If the comparison result is normal, perform infrared induction detection on the edge of the banknote image to obtain the infrared edge features and their signal intensities. Judge the distribution of the infrared edge features through a bar chart to generate stable or unstable signals. At the same time, select a group of infrared edge features, compare their variation rules with the signal intensities of the overall image edge, and generate regular or irregular variation signals. If stable and regular variation signals are obtained simultaneously, determine that the banknote verification is normal and generate a normal banknote verification signal; otherwise, generate an abnormal banknote verification signal. If the comparison result is abnormal, obtain the corresponding abnormal edge features, select a group of normal edge features as the target object, find the two closest points between the abnormal and normal edge features as feature points, calculate the distance between the two points and compare it with the standard distance to generate a secondary analysis signal. Obtain the secondary analysis signal, obtain all the pixel points of the image corresponding to the abnormal edge features, and obtain the corresponding pixel points of the pixel points. At the same time, obtain the gray level after image quantization, construct a gray-level co-occurrence matrix, and calculate the texture correlation value corresponding to the abnormal edge features. Then compare it with the preset value to generate a normal or abnormal banknote verification signal.

[0007] As a further solution of the present invention, the specific method for performing ultraviolet fluorescence detection on the extracted edge features is as follows: Label the obtained edge features as i, and i = 1, 2,..., j. Here, i represents the type label of the edge features, and j represents the specific type corresponding to the edge features. Then perform ultraviolet fluorescence detection on the obtained edge features. Specifically, irradiate the banknote with ultraviolet light to obtain the fluorescent edge features.

[0008] As a further solution of the present invention, the specific method for generating abnormal or normal comparison results is as follows: Obtain the fluorescent edge corresponding to the genuine banknote as the comparison feature, and compare it with the fluorescent edge features. If the fluorescent edge features are the same as the comparison feature, generate a normal comparison result; otherwise, generate an abnormal comparison result.

[0009] As a further solution of the present invention, the specific method for judging the distribution of infrared edge features through a bar chart to generate stable or unstable signals is as follows: Perform infrared induction detection on the edge features to obtain the infrared edge features. Obtain all the infrared edge features and label them as n, and n = 1, 2,..., m, where m is the type of infrared edge features. Obtain the edge signal intensity corresponding to the infrared edge feature n and denote it as Kn, and judge the distribution of the infrared edge feature based on the height of the bar chart. Obtain the height difference of the edge signal intensity corresponding to the genuine banknote, and compare the two. If the two are the same, generate a stable signal; otherwise, if the two are different, generate an unstable signal. Specifically, the same here means that the difference between the two is within a preset range. Otherwise, if it is not within the preset range, it means they are different, and the value of the preset range is set by the operator based on a large amount of data.

[0010] As a further solution of the present invention, the specific manner of generating the regular or irregular change signal is as follows: Obtain the edge signal intensity Kn corresponding to the infrared edge feature n. At the same time, take one group of infrared edge features as the analysis object, obtain the edge signal intensity Kn of the analysis object and the change rule of the analysis object, and compare the two. If the change rules of the two are the same, it means that the edge signal intensity changes regularly, and a regular change signal is generated; otherwise, if the change rules of the two are different, it means that the edge signal intensity changes irregularly, and an irregular change signal is generated.

[0011] As a further solution of the present invention, the specific manner of generating the secondary analysis signal is as follows: Obtain any group of normal edge features and denote them as the target object. Establish a corresponding rectangular coordinate system with the center point of the current image, and represent the abnormal edge features and the target object in the form of coordinates. At the same time, determine the feature points corresponding to the abnormal edge features, and here the feature points are the points closest to the target object, and obtain the coordinate positions corresponding to the feature points. Similarly, obtain the coordinate positions of the feature points corresponding to the target object. Then, according to the coordinate distance formula Calculate the distance between the two feature points, and at the same time compare the distance with the standard distance value. If the distance is the same as the standard distance value, it means that the relative position of the abnormal edge feature meets the requirements, and a secondary analysis signal is generated; otherwise, if the distance is different from the standard distance value, it means that the relative position of the abnormal edge feature does not meet the requirements, and a banknote verification abnormal signal is generated.

[0012] As a further solution of the present invention, the specific manner of constructing the gray-level co-occurrence matrix is as follows: Obtain the abnormal edge features corresponding to the secondary analysis signal, and at the same time obtain the corresponding image. Then, obtain the pixel points in the image and denote them as a, where a = 1, 2,..., b, and b represents the number of pixel points. Represent the pixel point a as (i a , j a ). Then, obtain the preset direction and distance, and obtain the pixel point corresponding to the pixel point a, denoted as the point to be analyzed ( , ), where Indicates the coordinate offset from the current pixel (i a , j a ) to another relevant pixel in the vertical direction (row direction), Indicates the coordinate offset from the current pixel (i a , j a ) to another relevant pixel in the horizontal direction (column direction), and and are determined by the direction and distance, and the number of occurrences of the gray - level value combinations of these two pixels is counted; Obtain the gray - level L after image quantization, and construct a gray - level co - occurrence matrix P of L×L. The element P(i a , j a ) in the matrix represents the frequency of occurrence of pixel pairs with gray - level values i and j under a given direction and distance.

[0013] As a further solution of the present invention, the specific method for generating the banknote verification normal or abnormal signal is as follows: According to the formula calculate the correlation value COR of the image, where and are the mean values of gray - level values i and j respectively, and are the standard deviations of gray - level values i and j respectively. At the same time, compare the calculated correlation value COR with a preset value; If the correlation value COR is greater than the preset value, it indicates that the texture feature of the current image has a high correlation with the texture feature of the genuine banknote image, and a banknote verification normal signal is generated. On the contrary, if the correlation value COR is less than the preset value, it indicates that the texture feature of the current image has a low correlation with the texture feature of the genuine banknote image, and a banknote verification abnormal signal is generated.

[0014] The present invention provides a banknote verification method for a banknote detector based on edge positioning. Compared with the prior art, it has the following beneficial effects: By sequentially performing ultraviolet fluorescence detection and infrared induction detection, and combining with the systematic analysis of the detection results, the present invention can more comprehensively and accurately judge the authenticity of banknotes, carefully label the edge features and compare them with the fluorescence edge features of genuine banknotes. In the infrared induction detection, analyze the infrared edge features from two aspects of stability and regularity, greatly improving the accuracy of banknote verification, reducing the misjudgment and missed - judgment rates. For abnormal comparison results, not only judge the relative position relationship by calculating the distance between the abnormal edge feature and the normal edge feature, but also deeply analyze the texture feature of the abnormal edge, such as calculating the correlation value using the gray - level co - occurrence matrix and comparing it with the preset value, effectively identifying counterfeit banknotes with fine forgery and enhancing the ability of the banknote verification technology to cope with complex forgery means. Brief Description of the Drawings

[0015] Figure 1This is the flowchart of the method steps of the present invention. Detailed implementation manners

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Example 1, please refer to Figure 1 , the present application provides a banknote verification method based on edge positioning. The method specifically includes the following steps: Step S1: The banknote verification machine acquires an image of the banknote through an optical sensor, and at the same time uses an image processing algorithm to perform edge detection on the obtained image, extracts the edge features corresponding to the image, and the edge features obtained here include characters, patterns, shapes, and lines.

[0018] Specifically, when using an image processing algorithm (such as the Canny algorithm) for processing, first convert the color image into a grayscale image, use a Gaussian filter to smooth the grayscale image to reduce the noise in the image, calculate the gradients of the image in the horizontal and vertical directions through the Sobel operator, and then obtain the amplitude and direction of the gradients. The amplitude represents the intensity of the edge, and the direction represents the orientation of the edge. In the gradient amplitude image, check the neighboring pixels in the gradient direction of each pixel point. If the gradient amplitude of this pixel point is not a local maximum, set it to 0, thereby refining the edge. Set two thresholds (a low threshold and a high threshold), determine the pixel points with a gradient amplitude greater than the high threshold as strong edges, eliminate the pixel points with a gradient amplitude less than the low threshold, and retain the pixel points between the two if they are connected to the strong edges, otherwise eliminate them, and further obtain the edge features of the image, which are represented by the following code: import cv2 import numpy as np import matplotlib.pyplot as plt # Read the image image = cv2.imread('your_image.jpg') # 1. Grayscale the image gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 2. Gaussian smoothing blurred = cv2.GaussianBlur(gray, (5, 5), 0) # 3. Edge detection using the Canny algorithm # Here, the low threshold is set to 50 and the high threshold is set to 150. You can adjust according to the actual situation edges = cv2.Canny(blurred, 50, 150) # Display the original image and the edge detection result plt.subplot(121),plt.imshow(cv2.cvtColor(image,cv2.COLOR_BGR2RGB)) plt.title('Original Image'), plt.xticks([]), plt.yticks([]) plt.subplot(122), plt.imshow(edges, cmap='gray') plt.title('Edge Image'), plt.xticks([]), plt.yticks([]) plt.show(). Step S2: Detect the obtained edge features through ultraviolet fluorescence detection. By comparing with the edge features of genuine banknotes, generate normal comparison results and abnormal comparison results.

[0019] Specifically, label the obtained edge features as i, where i = 1, 2,..., j. Here, i represents the type label of the edge features, and j represents the specific type corresponding to the edge features. Then, perform ultraviolet fluorescence detection on the obtained edge features. Specifically, irradiate the banknote with ultraviolet light. The fluorescent anti-counterfeiting marks on the banknote will emit fluorescence of a specific color and intensity under the excitation of ultraviolet light. For example, in the case of the Chinese yuan, fluorescent patterns at certain specific positions will clearly appear under ultraviolet light, and their edges are clear and regular, and the fluorescent edge features are obtained. At the same time, obtain the corresponding fluorescent edges of the genuine banknote as the comparison features. The fluorescent edges of the genuine banknote are stored in the banknote verification system of the banknote detector from the beginning. Here, the comparison features are of the same type as the obtained image features. Then, compare the two; If the fluorescent edge features are the same as the comparison features, it indicates that the fluorescent edge features are initially normal, and a normal comparison result is generated. If the fluorescent edge is different from the comparison features, it indicates that the fluorescent edge features are initially abnormal, and an abnormal comparison result is generated.

[0020] Step S3: Analyze the obtained normal comparison results, and analyze in combination with the infrared edges obtained through infrared induction detection. The specific analysis method is as follows: While performing ultraviolet fluorescence detection on edge features, infrared induction detection is carried out on the edge features to obtain infrared edge features. Specifically, the banknote is irradiated with infrared light. Since different materials of the banknote have different absorption and reflection characteristics of infrared light, the edge of the banknote will present a unique infrared image under infrared light. Then, the stability and regularity of the infrared edge features are analyzed. First, all infrared edge features are obtained and labeled as n, and n = 1, 2, …, m, where m is the type of infrared edge features; The specific method for analyzing the stability of infrared edge features is as follows: Obtain the edge signal intensity corresponding to the infrared edge feature n and denote it as Kn. At the same time, use a bar chart to represent the edge signal intensity Kn. Specifically, use the number n of the infrared edge feature as the abscissa and the corresponding edge signal intensity Kn as the ordinate to draw a bar chart, and judge the distribution of the infrared edge features based on the height of the bar chart. Obtain the height difference of the edge signal intensity corresponding to genuine banknotes and compare it with the height difference corresponding to the current infrared edge feature. If the two are the same, it means that the distribution of the infrared edge features is stable and a stable signal is generated. On the contrary, if the two are different, it means that the distribution of the infrared edge features is unstable and an unstable signal is generated; specifically, the same here means that the difference between the two is within a preset range. On the contrary, if it is not within the preset range, it means they are different, and the value of the preset range is set by the operator based on a large amount of data.

[0021] The specific method for analyzing the regularity of infrared edge features is as follows: Obtain the edge signal intensity Kn corresponding to the infrared edge feature n. At the same time, take a group of infrared edge features as the analysis object, obtain the change rule of the edge signal intensity Kn of the analysis object, and at the same time obtain the change rule of the corresponding genuine banknote of the analysis object. Generally speaking, for parts related to the anti-counterfeiting design of banknotes, such as specific ink printing areas, the edge intensity of their infrared signals will show a specific change rule, such as gradually weakening from the center to the edge, etc., and compare the two. If the change rules are the same, it means that the edge signal intensity changes regularly and a regular change signal is generated. On the contrary, if the change rules are different, it means that the edge signal intensity changes irregularly and an irregular change signal is generated; For example, the signal intensity Kn of the infrared edge feature of the security thread is measured at different scanning angles (scanning is performed every 10° from 0° to 360°). Through data analysis, it is found that the signal intensity Kn of the security thread of genuine banknotes shows a periodic change pattern: when the scanning angle is near 30°, 120°, 210°, and 300°, the signal intensity reaches the peak, and the peak intensity is between 80 and 90; when the scanning angle is near 60°, 150°, 240°, and 330°, the signal intensity reaches the trough, and the trough intensity is between 20 and 30, and the change period is 90°. This pattern is recorded in the genuine banknote change pattern database.

[0022] Analyze the stability and regularity of the comprehensive infrared edge feature. If both are satisfied, and here it is expressed as generating a stable signal and a regularly changing signal, it means that the result of the current banknote verification is normal, and a banknote verification normal signal is generated. Conversely, if any one of the two groups is not satisfied, it means that the result of the current banknote verification is abnormal, and a banknote verification abnormal signal is generated; Step S4: Analyze the obtained abnormal comparison result, determine the abnormal edge feature by comparison, obtain any group of normal edge features as the target object, and at the same time analyze the correlation between the target object and the abnormal edge feature. The specific analysis method is as follows: Then establish a corresponding rectangular coordinate system with the center point of the current image, and represent the abnormal edge feature and the target object in the form of coordinates. At the same time, determine the feature points corresponding to the abnormal edge feature. Here, the feature points are the points closest to the target object, and obtain the coordinate positions corresponding to the feature points. Similarly, obtain the coordinate positions of the feature points corresponding to the target object. Then, according to the coordinate distance formula Calculate the distance between the two feature points, and at the same time compare the distance with the standard distance value. The standard distance value is calculated and determined from the distances of multiple groups of genuine banknotes. If the distance is the same as the standard distance value, it means that the relative position of the abnormal edge feature meets the requirements, and a secondary analysis signal is generated. Conversely, if the distance is different from the standard distance value, it means that the relative position of the abnormal edge feature does not meet the requirements, and a banknote verification abnormal signal is generated; Specifically, various edge features of genuine banknotes have a relatively fixed positional relationship in space. For example, the edge of the serial number on the RMB should maintain a relatively fixed spacing and positional relationship with the edge of the surrounding decorative pattern. If the edge of the serial number appears abnormal and the spacing from the edge of the decorative pattern suddenly becomes larger or smaller, further analysis is required.

[0023] Then process the generated secondary analysis signal. When judging the texture feature of the abnormal edge feature, when judging the texture feature, analyze by establishing a corresponding gray-level co-occurrence matrix. The specific analysis method is as follows: Obtain the abnormal edge features corresponding to the secondary analysis signal, and at the same time obtain the corresponding image. Then, obtain the pixel points in the image and denote them as a, where a = 1, 2, …, b, and b represents the number of pixel points. And represent the pixel point a as (i a , j a ). Then, obtain the preset direction and distance, and obtain the pixel point corresponding to the pixel point a, denoted as the point to be analyzed ( , ), where represents the coordinate offset from the current pixel (i a , j a ) to another relevant pixel in the vertical direction (row direction), represents the coordinate offset from the current pixel (i a , j a ) to another relevant pixel in the horizontal direction (column direction), and and are determined by the direction and distance, and count the number of occurrences of the gray value combinations of these two pixels; Obtain the gray level L after image quantization, and construct the gray co-occurrence matrix P of L×L. And the element P(i a , j a ) in the matrix represents the frequency of the pixel point pairs with gray values i and j appearing under the given direction and distance. For example, if the pixel pair with gray values 5 and 7 appears 10 times in the image under the specified direction and distance, then P(5, 7) = 10. After traversing all the pixels, obtain the complete gray co-occurrence matrix. Then, calculate the correlation value COR of the image according to the formula , where and are the means of the gray values i and j respectively, and are the standard deviations of the gray values i and j respectively. At the same time, compare the calculated correlation value COR with the preset value, and the specific value of the preset value is calculated according to the gray value corresponding to the genuine banknote image; If the correlation value COR is greater than the preset value, it means that the texture feature of the current image has a high correlation with the texture feature of the genuine banknote image, and generate a normal banknote verification signal. On the contrary, if the correlation value COR is less than the preset value, it means that the texture feature of the current image has a low correlation with the texture feature of the genuine banknote image, and generate an abnormal banknote verification signal.

[0024] Embodiment 2. This embodiment is implemented on the basis of Embodiment 1, and the difference from Embodiment 1 is as follows: When analyzing the abnormal edge texture features in this embodiment, by calculating the contrast of the abnormal edge image, and analyzing and judging through the contrast, generate a normal banknote verification signal and an abnormal banknote verification signal, and the specific calculation formula is 。

[0025] Embodiment 3. This embodiment is implemented on the basis of Embodiment 1, and the differences from Embodiment 1 are as follows: When analyzing the abnormal edge texture features in this embodiment, the entropy of the abnormal edge image is calculated, and analysis and judgment are performed through the calculated entropy to generate a banknote verification normal signal and a banknote verification abnormal signal. The specific calculation formula is 。

[0026] For some data in the above formula, only their numerical values are taken for calculation, and the parameter units are not substituted for calculation. At the same time, the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0027] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A banknote verification method based on edge positioning, characterized in that: The method specifically comprises the following steps: Use the Canny algorithm to detect the edge of the acquired banknote image and extract edge features; Perform ultraviolet fluorescence detection on the extracted edge features, compare the obtained fluorescent edge features with the genuine banknote comparison features, and generate abnormal or normal comparison results; If the comparison result is normal, infrared sensing detection is performed on the edge of the banknote image to obtain infrared edge features and their signal strengths, and the distribution of infrared edge features is judged through a bar graph to generate a stable or unstable signal. At the same time, a group of infrared edge features are selected to compare the change pattern of the edge signal strength with that of the overall image to generate a regular or irregular change signal. If both stable and regular change signals are obtained, it is determined that the banknote verification is normal and a normal banknote verification signal is generated, otherwise an abnormal banknote verification signal is generated. If the comparison result is abnormal, obtain the corresponding abnormal edge features, select a group of normal edge features as the target object, find the two closest points between the abnormal and normal edge features as feature points, calculate the distance between the two points and compare it with the standard distance, and generate a secondary analysis signal; Obtain the secondary analysis signal, obtain all the pixel points of the image corresponding to the abnormal edge feature, obtain the corresponding pixel points of the pixel points, and obtain the grayscale of the image after quantization, construct the grayscale co-occurrence matrix, and calculate the texture correlation value corresponding to the abnormal edge feature, and compare it with the preset value to generate a normal or abnormal signal for banknote verification.

2. The banknote verification method of a banknote detector based on edge positioning according to claim 1, characterized in that: The specific method of performing ultraviolet fluorescence detection on the extracted edge features is: The obtained edge features are labeled as i, where i=1, 2, ..., j, and i here represents the type of edge feature, while j represents the specific type corresponding to the edge feature. The obtained edge features are then subjected to ultraviolet fluorescence detection, specifically using ultraviolet light to irradiate the banknote to obtain fluorescent edge features.

3. The banknote verification method of a banknote detector based on edge positioning according to claim 1, characterized in that: The specific method of generating abnormal or normal comparison results is: The fluorescent edge corresponding to the real banknote is obtained and recorded as a comparison feature, and compared with the fluorescent edge feature. If the fluorescent edge feature is the same as the comparison feature, a normal comparison result is generated, otherwise, an abnormal comparison result is generated.

4. The banknote verification method of a banknote detector based on edge positioning according to claim 1, characterized in that: The specific method of generating a stable or unstable signal by judging the infrared edge feature distribution through a bar graph is: Perform infrared sensing detection on the edge features to obtain infrared edge features, obtain all infrared edge features and label them as n, where n=1, 2, ..., m, where m is the type of infrared edge feature; The edge signal strength corresponding to the infrared edge feature n is obtained and recorded as Kn. The distribution of the infrared edge feature is judged based on the height of the bar graph. The height difference of the edge signal strength corresponding to the real banknote is obtained, and the two are compared. If the two are the same, a stable signal is generated. Otherwise, if the two are different, an unstable signal is generated.

5. The banknote verification method of a banknote detector based on edge positioning according to claim 1, characterized in that: The specific method of generating the regular or irregular change signal is: The edge signal strength Kn corresponding to the infrared edge feature n is obtained. At the same time, one group of infrared edge features is used as the analysis object, and the edge signal strength Kn of the analysis object and the change law of the analysis object are obtained and compared. If the change laws of the two are the same, a regular change signal is generated. Otherwise, if the change laws of the two are different, an irregular change signal is generated.

6. The banknote verification method of a banknote detector based on edge positioning according to claim 1, characterized in that: The specific method of generating the secondary analysis signal is: Take any set of normal edge features as the target object, establish the corresponding rectangular coordinate system with the center point of the current image, and express the abnormal edge features and target objects in the form of coordinates. At the same time, determine the feature points corresponding to the abnormal edge features, and obtain the coordinate positions corresponding to the feature points. Similarly, obtain the coordinate positions of the feature points corresponding to the target object, and then use the coordinate distance formula to calculate the distance between the abnormal edge features and the target object. The distance between the two feature points is calculated and compared with the standard distance value. If the distance is the same as the standard distance value, a secondary analysis signal is generated. Otherwise, if the distance is different from the standard distance value, an abnormal banknote verification signal is generated.

7. The banknote verification method of a banknote detector based on edge positioning according to claim 1, characterized in that: The specific method of constructing the gray level co-occurrence matrix is: Obtain the abnormal edge features corresponding to the secondary analysis signal, and obtain the corresponding image at the same time. Then obtain the pixel points in the image and record them as a, and a=1, 2, ..., b, where b represents the number of pixels, and the pixel point a is represented as (i a , j a ), then get the preset direction and distance, get the pixel point corresponding to pixel point a, and record it as the point to be analyzed ( , ), and count the number of occurrences of the gray value combination of these two pixels; Get the gray level L of the quantized image and construct an L×L gray level co-occurrence matrix P, where the elements P(i a , j a ) represents the frequency of occurrence of a pair of pixels a with grayscale values ​​i and j in a given direction and distance.

8. The banknote verification method of a banknote detector based on edge positioning according to claim 1, characterized in that: The specific method of generating a normal or abnormal signal for currency verification is: According to the formula The correlation value COR of the image is calculated, where and are the means of grayscale values ​​i and j respectively, and The standard deviation of the gray values ​​i and j respectively, and the calculated correlation value COR is compared with the preset value; If the correlation value COR is greater than the preset value, a normal banknote verification signal is generated. Conversely, if the correlation value COR is less than the preset value, an abnormal banknote verification signal is generated.

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