The Colour of Soil. De Nederlandsche Bank NV Tom Buitelaar. Prepared for. DNB Cash Seminar 2008 Amsterdam February 2008

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1 The Colour of Soil De Nederlandsche Bank NV Tom Buitelaar Prepared for DNB Cash Seminar 2008 Amsterdam February Introduction The following assumptions are made in this paper: - the performance of soil detection is poor - soil is a major contributor to the general fitness of banknotes Therefore I will concentrate on the soiling aspect. Some other aspects, especially the so called mechanical defects are relatively easy to detect, whereas other defects like stains, limpness in general show a high correlation with the soiling level. In the first chapter of this paper a number of properties are presented that have a potential relation with soil detection. After this theoretical approach the practical results of a comparative study of 8 different soil and fitness sensors are discussed, followed by a consideration of this theoretical and practical approach. From this consideration some guidelines are determined for an optimal performance of soil detection with the current technology. However as the current technology is not sufficient for a satisfying performance some solutions that are beyond the current technologies are proposed. Testsets In this paper many references to tests and measurements are made. Almost all of the measurements, calculations and conclusions have been based on testsets for denominations 5 to 50. The testsets that have been used are banknotes out of circulation that have been carefully visually selected into 5 classes, class 1 being superfit, as good as new, class 2 are good fit notes, class 3 are acceptable fit notes, notes of this class are fit enough for another round in circulation, but if they are destroyed it should not be considered as a great loss. Class 4 notes are the normal unfit notes and class 5 are super unfit notes, which should not reappear under any circumstances again in circulation. Each testset consists of 500 notes, 100 notes per class. Before using these sets the serial numbers have been registered of each individual note as well as the optical properties that have been measured with a photospectrometer. 2 Optical ways to determine soil on a banknote There are numerous ways to recognize soil on banknotes. The majority of these methods are based on optical measurements. There are other methods known for instance sound or ultrasound or even X-ray based recognition of soil, but it is proposed to first determine the potential of the relatively simple optical methods before we draw the conclusion more difficult and advanced techniques are needed.

2 2.1 Measurement of reflected light This is a method that is used already since the beginning of automated sorting of banknotes. At least in the 1 st sorting machine that was used at DNB in the early seventies, a sensor with this principle was already present. It must be said that there were already many complaints of the sorting quality of this sensor. As optics and imaging were only at the beginning of their march forward, this sensor measured the total amount of reflected light in one step. No camera, no individual pixels, just one measurement with a photo sensitive element. As the results of the simple solutions were not satisfying we could observe the introduction of digital imaging. In the early eighties at DNB the BFIS (Banknote Fitness Inspection System) sensor for Toshiba sorting machines was developed. This sensor was based on a kind of line camera, an array of 64 photodiodes. This BFIS produced images with a resolution of 2x2 mm (2 3 kpixels per banknote). Notwithstanding numerous efforts to improve, the sorting quality remained as poor as it was before. Until today many sensors are based on this principle, albeit that the resolution has steadily increased over time, as many developers hope that soil can be better identified with high resolution. Of course these products are more advanced as the early sensors, an important step forward is the possibility of selecting specific regions. However the current situation is that the performance of even the newly developed sensors still is considered as unsatisfying. Let us look at the performance of such a current design, fig 1 shows an image that nowadays can be captured on a high speed sorting system. The black square in the white area indicates the area where the reflection of the banknote could be measured. Determining the optimal measurement area is a problem in itself, a white area seems to be the best choice, however the largest white area often contains the watermark and the variations due to the watermark will hinder a proper reflection measurement. To illustrate the potential performance this technique is used for a subset of 10 notes out of the testset described before Fig 1, example of image captured in modern sensors Table 1 shows the results of the measurement for the 5 classes of notes.

3 Table 1 Reflectance measured in measurement window of fig 1. Class 1 Class 2 Class 3 Class 4, Class 5 Superfit Fit Acceptable Unfit Super Unfit 1 171,27 167,02 161,17 152,56 144, ,92 167,62 163,12 152,27 138, ,73 166,56 160,34 158,08 140, ,32 161,05 158,19 148,84 148, ,46 166,14 159,51 155,01 149, ,87 169,76 159,11 156,74 150, ,52 168,08 162,69 151,74 153, ,85 165,30 154,61 156,29 150, ,02 165,94 152,34 143,51 150, ,16 166,31 156,24 154,04 138,31 Average 169,91 166,38 158,73 152,91 146,47 The general assumption that soiled notes reflect less light is confirmed in this table especially as the average of the measurements is observed. However the spread within a fitness class cannot be neglected, for instance individual notes of classes 3 and 4 show an overlap, which means that it is not possible to discriminate between the classes acceptable and unfit. It is inevitable to either destroy acceptable notes or to allow unfit notes to reappear in circulation using these measurements. An easy to use indicator is the correlation factor between the reflection level and the fitness class number. The correlation factor for these 50 notes in this example is -0.92, which is quite high and indicates a reasonable performance. For the following part of this paper this correlation factor is used as a performance indicator for the various techniques. This indicator allows us to compare the behaviour of the various techniques. The next table shows this correlation factor for similar sets of 5 * 10 notes for other denominations using the pictures made by this sensor: Table 2 Correlation between class and reflection level for different denominations Denom Corr. Factor class vs. refl E5-0,80 E10-0,92 E20-0,85 E50-0,78 This table shows that a reasonable performance that can be observed for euro 10 but the observed correlations for E5 and E50 predict a more problematic sorting performance for these denominations. 2.2 Measurement of transmitted light Another possible option to determine the soil level of banknotes is to measure the amount of transmitted light through the banknote. In this case the more or less natural choice is the transmission in the near infrared part of the spectrum. This choice is obvious as an important part of the banknote is transparent in infrared, thus offering a larger area to be inspected. In fig 2, two notes are presented using this technique, the left half representing a clean note, the right part representing a soiled note. It is obvious that the right half of figure is significantly darker then the left half and that there is a potential relation between soil level and the amount of light transmitted through the note.

4 The enclosed yellow area and the number below indicate the measurement area and the average level of transmitted light in this area. Table 3 shows the amount of transmitted light of the same notes as used before but now for the transmission in infrared Fig 2 Example of images in transmitted IR light Table 3 Transmission measured in measurement window of fig 2 Class 1 Class 2 Class 3 Class 4 Class 5 Super Fit Fit Acceptable Unfit Super unfit Average When tables 1 and 2 are carefully compared it can be observed that the overlap between the various classes is larger for the transmission results of table 3 then the reflection results of table 1, indicating that sorting based on this principle will give lower sorting quality then a measurement based on reflection. The same trend can be observed when looking at the correlation factors. For the reflection case a correlation factor of was calculated, for the transmission case a correlation factor of is observed. Table 4 shows the correlation factors for both types of measurements for denominations 5-50

5 Table 4 Correlation factors for class number and measured reflection and transmission Corr. Factor class vs. Denom Refl Reflection Transmission E5-0,80-0,68 E10-0,92-0,85 E20-0,85-0,76 E50-0,78-0,57 The conclusion can be that the reflection measurement is better suited as a measurement property then the transmission measurement. 2.3 Measurement of local variations of reflected or transmitted light Another potential method to measure soil and wear is to look at the local variations, as shown in fig 3; soil is not evenly distributed but is concentrated on fold lines, small areas of heavier soiling, but also in areas with relatively little soil. Fig 3 Local variations in a soiled note. Potential indicators for the determination of the variation are the standard deviation or the minimum or the maximum points measured in the measurement area. Other, more enhanced properties are possible, such as the average of the 5 % highest or the 5 % lowest values measured in the measurement area. Table 5 gives an indication of the performance of the various possibilities for measuring the variation. Again in this table the correlation factor is presented between the class number and the measured value for the same 50 banknotes that were already used before. Table 5 Correlation factors for class number and some variation indicators Correlation factors for reflection Correlation factors for transmission Avg. stdev min max Avg stdev min max E5-0,800 0,551-0,632-0,737-0,682-0,092-0,684-0,662 E10-0,921 0,757-0,697-0,833-0,849-0,031-0,816-0,808 E20-0,853 0,253-0,414-0,800-0,762-0,242-0,741-0,663 E50-0,779 0,104-0,109-0,693-0,573-0,309-0,489-0,588 The general tendency seems to be that an accepted indicator of variation, ie. the standard deviation has worse results then the maximum value (reflection case) or the minimum (most of the transmission cases) However the average values especially for the reflection still show the best results.

6 2.4 Measurement of gloss Experience at the Banque de France with a formal gloss measurement indicate that the gloss of a banknote might be an indicator for the soil level. At this moment this measurement has not been done with the testset that has been used at DNB, so comparable measurements are not yet available. 2.5 Measuring the spectral properties As mentioned in the introductory chapter, the spectral properties of all notes used in the testset have been determined with a spectrophotometer. Figure 3 shows the spectral reflectance of the 5 soil classes for E 5. The 5 curves represent the average of the 100 notes belonging to each soil class. Spectral reflection E 5 for 5 soilclasses AVG SFIT AVG FIT AVG Acceptable AVG UNFIT AVG SUNFIT nm 390nm 420nm 450nm 480nm 510nm 540nm 570nm 600nm 630nm 660nm 690nm 720nm Fig 3 Spectral reflection curves for E5 The Y axis represents the percentage of light that is reflected for a given wavelength. In principle the value cannot exceed the 100 % level. The X-axis represents the wavelengths of the visual spectrum of the light that ranges from 360 to 740 nm. It is obvious that in the red and infrared part of the spectrum the difference between the classes is very small, which means that it will be difficult to determine the soil level if we base our measurements on this part of the spectrum. The green part of the spectrum ( nm) will result in a better recognition of the soiled notes, but it is clear from this curves that the best results can be expected when we look at the blue and more specific, the indigo part of the spectrum ( nm), below 420 the curves tend to become less stable. As mentioned before the 5 curves represent the average of 100 notes for each class, the individual curves will be around this average as is shown in fig 4, where the cloud of individual curves are drawn around the green average for the class 3 soil level.

7 Fig.4 Cloud of individual curves around the average The same behaviour of the spectral curves that was shown for E5 can be seen for the other denominations: a small and negligible difference in the red and infrared part of the spectrum and largest difference between clean and soiled notes in the nm range. (Figs 5 7 for denominations E10 E50) Spectral reflection E10 for 5 soiling classes nm 400nm 440nm 480nm 520nm 560nm 600nm 640nm 680nm 720nm Fig 5 Spectral reflection curves for E10 AVG SFIT AVG FIT AVG Acceptable AVG UNFIT AVG SUNFIT

8 Spectral reflection E20 for 5 soil classes nm 400nm 440nm 480nm 520nm 560nm 600nm 640nm 680nm 720nm Fig 6 Spectral reflection curves for E20 AVG SFIT AVG FIT AVG Acceptable Avg Unfit Avg SUnfit Spectral reflection E50 for 5 soiling classes nm 390nm 420nm Fig 7 Spectral reflection curves for E50 450nm 480nm 510nm 540nm 570nm 600nm 630nm 660nm 690nm 720nm AVG SFIT AVG FIT AVG Acceptable AVG UNFIT AVG SUNFIT The general trend is for all 4 denominations is the same, little or no difference in reflection between the classes in the red or infrared area, the largest difference in the deep blue or indigo part of the spectrum. Effectively this conclusion can be interpreted as the fact that soil on banknotes is not uniformly grey but ha a distinct yellowish/brownish tint. The optimal area to detect soiling therefore is the blue part of the spectrum. This can be achieved using either using a blue illumination for the soil sensor or using a form of white light and using a blue filter in front of the light capturing device. Like we did for the other potential soil detection principles we can determine the correlation factors for the spectral properties as is shown in table 6:

9 Table 6 Correlation factors for some spectral properties L*(D65) a*(d65) b*(d65) Brightness(ISO) 440nm 550nm 650nm E5-0,735 0,152 0,882-0,854-0,864-0,719-0,376 E10-0,797-0,195 0,868-0,870-0,877-0,760-0,648 E20-0,700-0,664-0,911-0,891-0,900-0,573-0,441 E50-0,699 0,108-0,780-0,837-0,842-0,732-0,301 Unfortunately we cannot directly compare the correlation factors from this table which has been derived from 100 notes per soiling class with the correlation factors derived in paragraphs as the correlation factors in these chapter have been derived from 10 banknotes per soil class. From the correlation factors shown in table 6 we can learn that soil detection based on colour information is to be preferred over the general reflection level as is represented by the L* parameter. More specifically we see from the table that the blue parameters (b*, Brightness and the reflection at 440nm, all perform significantly better then the general L* or the red 650nm and green 550nm parameter. 2.7 Reading the serial number or other sign and determining the age of the sorted note A totally different source of information that may be an indicator for the soil level is the age of the banknote. The age of the banknote in this context must be seen as the time that has elapsed since an individual banknote has been brought in circulation and the time of sorting. With the help of the numberreading system of DNB and some special computer programs we are able to determine with a reasonable precision the date a particular banknote has been brought in circulation. In the pre euro era, we could determine this date exactly, but since the introduction of the euro the error margin has increased to 1 or 2 months. We may expect that a banknote that is in circulation for a shorter period will be cleaner then a banknote that is in circulation 3 or 4 years. In table 7 some characteristics of this parameter are shown: Table 7, Some characteristics of the age of banknotes Average week of birth (weeks since 1/1/2002) Correlation birthweek -class Super Fit Fit Acceptable Unfit Super Unfit E ,271 E ,518 E ,671 E ,665 As characteristics the average week of birth of the notes is presented per soiling class. The week of birth is determined by the number of weeks that have passed since the introduction of the euro on 1/1/2002. We see that the expected relation between week of birth and soiling level is present. In all cases we see that the more soiled a class is, the older the average age. For a short living denomination like euro 5, we can see that the difference in birth weeks between the various classes is smaller then the difference between the higher denominations. The older generations of euro 5 notes apparently have been destroyed already by now. We can also see that the older notes for higher denominations are still present in circulation. So as we may conclude there is a relation between class and age, we can also observe if we look at the correlation factor that this relation is not very strong.

10 The correlation factor for some of the optical properties is significantly better, so the best role for a soil detection based on age would be as a supportive factor. 3 Results of tests of different types of sorting machines. The tests described below have been performed on a number of sorting machines (G&D BPS200, BPS500 and BPS2000, Toshiba FS800 and FS1200, DLR CPS2000, DLR 7000 and Cobra). For this test we have had the full cooperation of the Banque de France and Banco d Espagna, both for access to the machine and with regards to the technical background. In the remainder of this paper the reference to these machines are coded as machines A - H. An important aspect of the test has been to get an idea of the operating principle. The spectral response of the sensor had our special interest as the results of the theoretical approach showed that the spectral area a sensor is active, is likely to have an impact on the performance of a sensor. Unfortunately it was not possible to determine this response for all machines. The main emphasis has been on the behaviour of the machines with real notes. Each machine has been tested with at least 50 notes out of the testset described before for each denomination. For each individual note the soil value as calculated by the sensor has been recorded and this soil level has been combined with the other data that was collected for each note. Apart from 1 sensor that is based on a very simple reflection measurement by a single photocell, all other sensors are based on more modern camera solutions. Depending on the period a particular sensor was developed, the resolution may vary. We can see a trend that sensors developed during the last 3-4 years have such a camera resolution that these sensors can recognize the serial numbers on the banknotes. Another important aspect of a fitness sensor is the illumination, is a halogen lamp used or is it a LED illumination. The shape of the illumination turned out to be important as well so for some sensors this has been a subject of comparison as well. 3.1 Spectral response The spectral response is measured with the help of 30 sheets of commercial paper with known spectrally measured colours. These coloured sheets are transported on the different sorting machines and the response of the machine for each piece of paper has been recorded. As response the soillevel as calculated by the sensor has been usedl Combining both the spectral properties and the soil level for these coloured banknotes results in a spectral response curve for the different sorting machines that is shown in fig. 8:

11 Spectral response different sensors 1,2 1 0,8 0,6 0,4 0,2 0 A B F H C -0,2-0,4 360nm 380nm 400nm 420nm 440nm 460nm 480nm 500nm 520nm 540nm 560nm 580nm 600nm 620nm 640nm 660nm 680nm 700nm 720nm 740nm Fig 8 Spectral response of 5 systems In fig.8 5 curves of 5 machines from 2 suppliers are presented. Unfortunately it has not yet been possible to determine the response curve for all sensors using this technique. In this graph 4 types of spectra can be recognized as it is obvious that sensor A and sensor F have exactly the same characteristics and come from the same supplier. However the curves for sensor A and F also demonstrate the reliability of the technique to determine the spectral response and it may be assumed that both sensors A and F will behave very similar in the practice of sorting banknotes. Furthermore these (4) curves demonstrate the fact that the different suppliers do not share a common view on the detection of soiled banknotes. The supplier of machine A and F puts his bets on the orange red infrared part of the spectrum for his soil recognition, whereas the supplier of sensor B is of opinion that we should concentrate in the deep blue area of the spectrum. The supplier of H has not a particular preference and just looks at the whole visual spectrum. 3.2 Correlation between detector response and soiling class For each individual sensor, the behaviour for each of the 4 denominations has been tested with at least 10 banknotes from the 5 classes out of the testset. One sensor has seen all the testnotes and therefore can act as a reference point for the results of the other sensors. With a limited set of banknotes it is always possible that one sensor has to deal with a relatively simple set where the soil level is relatively easy to determine whereas other sensors may have to deal with a difficult set for which the soil level is not easy to determine. In such a situation the reference sensor also will have better results with the easy set and will show not so good performance with the difficult set. Table 8 shows the correlations for the various sensors and also the correlation for the reference sensor with the identical notes. The last column displays the ratio between both correlations for the euro 5 results:

12 Table 8 Correlation between soiling class and sensor result for E 5 Sensor Correlation sensor X Correlation reference sensor Ratio sensor X reference sensor A -0,829-0,895 0,926 B -0,903-0,903 1,000 D -0,850-0,883 0,962 E 0,924-0,956 0,967 F -0,847-0,903 0,938 G 0,866-0,913 0,949 H 0,836-0,892 0,937 In this table the calibration principle is illustrated with the results for sensor E. For sensor E a high correlation (0,924) can be observed which might lead to the conclusion that this sensor is the best of all. However if the results for the same testset with the reference sensor (B)are taken into account as well, it is obvious that the reference sensor also has a very high correlation for this particular set of 50 notes, apparently this test set is rather easy to recognize by any sensor. The last column, the ratio between the correlation measured by a sensor and the correlation measured by the reference sensor therefore seems to be the best parameter to determine the sorting capabilities of a particular sensor. By coincidence it happens to be that the reference sensor seems to perform better then all of the other tested sensors at least for this denomination. Table 9 Ratios correlation of sensor and reference sensor E5 E10 E20 E50 Average A 0,926 0,948 0,471 0,729 0,768 B 1,000 1,000 1,000 1,000 1,000 D 0,962 0,952 0,897 0,928 0,935 E 0,967 0,941 0,984 0,896 0,947 F 0,938 0,976 0,571 0,962 0,862 G 0,949 0,998 0,755 0,879 0,895 H 0,937 0,941 0,884 0,931 0,923 In table 9 the results of all the sensors are shown for all denominations and again sensor B seems the best performer. However a correlation factor is not more then an indication of the quality of the different sensors and it is time to look at the real sorting behaviour. 3.3 Filter function of a detector If we want to compare the performance of sensors, a common benchmark must be defined that can be used for each of the sensors. A good benchmark may be found in the Framework that describes the performance criteria of sorting equipment used by commercial parties within the ESCB area. In this framework a criterion is given that allows 5 % of the unfit notes to be returned in circulation. Having a testset of 50 notes with 20 notes belonging to either the soiling class Unfit or Super Unfit, this criterion can be interpreted as 1 unfit note that may return into circulation and 19 that must be sent to unfit.in this situation a threshold must be determined that just stops the 2 nd unfit note from being brought in circulation. So the threshold is determined as the value just above the value of this 2 nd unfit note. An illustration of this concept is shown in table 10:

13 Table 10 Sorted form of table 1 Class 1 Class 2 Class 3 Class 4, Class 5 Superfit Fit Acceptable Unfit Super Unfit 1 167,16 161,05 152,34 143,51 138, ,92 165,30 154,61 148,84 138, ,52 165,94 156,24 151,74 140, ,85 166,14 158,19 152,27 144, ,02 166,31 159,11 152,56 148, ,32 166,56 159,51 154,04 149, ,87 167,02 160,34 155,01 150, ,27 167,62 161,17 156,29 150, ,46 168,08 162,69 156,74 150, ,73 169,76 163,12 158,08 153,70 The benchmark criterion defined before allows 1 note of the unfit classes to return into circulation. In the case as illustrated in the table above, this means that note number 10 of class 4 is allowed to return into circulation. However note 9 of the same class must be stopped. This can be done by applying a threshold for the reflection value of 156,75. With this threshold note 9 will be seen as a soiled note by the detector and therefore will be sent to unfit. However when this threshold is applied, notes 1 3 of the still acceptable class 3 will also be sent to unfit. So the performance of the sensor can be summarized as in table 11: Table 11 Filter function of soil sensor Class Fit Unfit Although the behaviour of the sensor in this case is not too bad, it is not perfect as we will have to destroy 3 notes with a still acceptable quality level. The perfect filter function therefore would look like table 12: Table 12 Filter function of a perfect soil sensor Class Fit Unfit It would be nice if a number could be assigned to this table to further summarize the behaviour of the sensor into a single number. A possible solution for this is to count the number of notes out of the classes 1 3 that have been judged as unfit by the sensor and divide this number of notes by the total number of notes for these 3 classes. With this solution a number ranging between 0 and 1 is obtained. A zero when the filter function is perfect and 0 notes must be destroyed to obtain the required result of 1 unfit note that may return in circulation. In the case shown in table 9, 3 notes must be destroyed, so the filter function can be characterized with the number 0,1, as we have to destroy 3 out of 30 notes.

14 However this function does not take into account the class of the note that was destroyed. Obvious the economic value of the loss of a superfit note that is destroyed is higher then the cost of the loss of a just acceptable note that already has done a considerable job in circulation. Therefore a filterfunction with a weight factor is proposed, for the remainder of this paper a weight of 3 for a superfit note is used, a weight of 2 for a fit note and a weight of 1 for the loss of an acceptable note. In the case of table 9 the result will be (0*3+0*2+3*1)/(10*3+10*2+10*1) = 3/60 or 0,05. In stead of interpreting this value as a percentage of visually fit notes that has to be destroyed, this factor is an indication of the value that must be destroyed to achieve the criterion. So in this case 5 % of the economic value of the fit notes in circulation must be destroyed to achieve the 5 % criterion. In table 13 the filter function describing the economic value of the fit notes that must be destroyed in order to meet the 5 % criterion is presented for all sensors and denominations: Table 13 Filter function for different sensors in comparison with reference sensor E5 Ref. sensor E10 Ref. sensor E20 Ref. sensor E50 Ref. sensor Avg Ref Average sensor A 0,083 0,017 0,050 0,083 0,683 0,000 0,417 0,250 0,308 0,088 B 0,112 0,112 0,075 0,075 0,061 0,061 0,148 0,148 0,099 0,099 D 0,033 0,000 0,033 0,017 0,067 0,000 0,051 0,119 0,046 0,034 E 0,083 0,050 0,140 0,000 0,050 0,000 0,167 0,100 0,110 0,038 F 0,083 0,000 0,017 0,017 0,500 0,100 0,167 0,217 0,192 0,083 G 0,133 0,133 0,052 0,121 0,542 0,008 0,157 0,111 0,221 0,093 H 0,317 0,167 0,033 0,017 0,217 0,133 0,000 0,000 0,142 0,079 Looking at this table it is obvious that some sensors need to destroy a large part of the value of the fit notes in order to reach the requirement of 5 % unfit. Especially sensor A requires ~ 30 % of the rest value of the fit notes in order to achieve this criterion. However also sensors F and G require twice the value that is necessary for the reference sensor. As in the correlation view, the reference sensor B also performs best using this filter technique. 3.4 Cost of poor performance To compare the cost associated with the less then optimal performance we will more closely look at the performance of 2 sensors B and G. With both these sensors significantly more then 50 testnotes per denomination have been tested. Sensor B has seen all 500 notes of a set; sensor G has sorted 300 notes of 2 sets. With this amount we can with a certain degree of precision determine the value that is associated with the sorting performance of these sensors. This cost is presented as the value of the destroyed notes per million sorted notes Cost/ Sensor B Sensor G E E E E For a high speed sorting machine with a capacity of notes/hour, it can be calculated that the cost of the poor performance is as high as 600 euro worth of destroyed notes per hour of operation.

15 4 Considerations I can be concluded from the results of the survey that there are a few machines that perform poorly, in contrast with one sensor that performs significantly better then the rest. Other machines are somewhere in between these 2 extremes. In order to find the best principle of soil detection, it would be nice if the differences in performance between the sensors could be explained. What are the characteristics of sensor B and what are the characteristics of sensors A, F and G. An important difference between sensor B and sensors A, F and G is the difference in spectral response. Spectral response different sensors 1,2 1 0,8 0,6 0,4 0,2 0 A B F H C -0,2-0,4 360nm 380nm 400nm 420nm 440nm 460nm 480nm 500nm 520nm 540nm 560nm 580nm 600nm 620nm 640nm 660nm 680nm 700nm 720nm 740nm Fig 9 Copy of graph of fig 8. For sensor G unfortunately this spectral curve is lacking, but fortunately a big difference between sensor B and sensors A and F can be seen in the response curve. Sensor B is definitely looking at the blue part of the spectrum, sensor A and F concentrate on the red and infrared part of the spectrum. From discussions with the supplier of sensor G I have the impression that also this sensor is looking in the red/ir part of the spectrum. Combined with the information that was found as the colour of soil (fig 10), it should not come as a big surprise to see this difference in performance. These curves give ample evidence that the colour of soil is yellow/orange and that it would be rather foolish to discriminate the soil in the red and near infrared part of the spectrum.

16 Spectral reflection E 5 for 5 soilclasses AVG SFIT AVG FIT AVG Acceptable AVG UNFIT AVG SUNFIT nm 390nm 420nm 450nm 480nm 510nm 540nm 570nm 600nm 630nm 660nm 690nm 720nm Fig 10 Copy of fig 3 I am convinced that a major part of the performance difference can be explained out of this aspect. This conclusion is further augmented by the fact that sensors D and E, sensors that follow sensor B in performance, also make use of blue LED s or a blue filter. Sensor H, a sensor that has a more or less flat spectral response, can be classified as performing in between both groups. Nevertheless the results show that sensor B still performs better then the other 2 that also make use of the blue illumination or the blue filtering. The main remaining difference between both sensors is the fact that the light capturing is done with a rather old fashioned photo diode, whereas sensor D and E make use of camera techniques that allow specific areas to be selected for inspection. One of the explanations may be the fact that with camera solutions we usually have to use a small depth of field in order to focus the light into the camera. As a consequence of this small depth of field the banknote is not allowed to have too much position variation perpendicular to the transport direction. The photodiode does not have this problem and therefore will have a more stable performance; vertical position variation does not have too much effect. Another important difference may be the illumination, for sensor B a ringshaped illumination of white LED s has been chosen. This has the effect that the amount of light that is reflected back to the photodiode is to a certain degree independent of crumpling of the notes. Normally depending on the angle of illumination (or the camera angle) a crumple will show as a lighter area on one side of the crumple and a darker area at the other side of the crumple.

17 Fig 11 Shading effect of crumples Given the relative small difference between fit notes and soiled notes, the shading effect can cause significant variation in the measured soil level. As can be seen in fig 11 the soil that is present on the banknote is almost completely hidden in the shading effects caused by illumination on one side of the camera. This effect will also be present when the banknote has a larger area that is not completely perpendicular with regards to the transport direction, eg. when in one of the corners a large folded corner is present. Fig 12. Eliminating the crumples in the image by using 2 sided illumination In fig 12 the effect of 2/ sided illumination is shown where an array of LED s is placed on both sides of the camera. It is obvious that the soil is far better visible (and therefore also measurable).

18 Concluding this chapter it can be concluded that the superior quality of soil detection in the blue and more specifically the deep blue part of the spectrum have been explained and demonstrated. It has also been demonstrated that even an advanced camera solution shows no improvement for soil detection in comparison with a well engineered photodiode solution. Maybe the opposite is true and may the photodiode solution have some inherent advantages such as the lack of depth of field problems and the better opportunity for applying optimal illumination. Another advantage is the fact that the solution is very cheap, can be small and that defining suitable parameters is strikingly easy. An important message: more pixels have no relation with better soil detection! 5 Reasons for poor performance in general In chapter 4 some possible reasons were mentioned why some detectors perform worse then other detectors, but even for the best performing sensor quite an amount of fit banknotes must be destroyed to satisfy certain sorting criteria. Even for the B sensor, 17 % of the still fit notes or about 10 % of the value of the fit notes must be destroyed. This figure is achieved with the performance criterion that 5 % of the unfit notes may still return in circulation. So although the best performing sensor is found, this does not mean that sensor B is the ultimate solution. The main reason for this still unsatisfying performance is the variation that can be observed in newly printed banknotes. This problem can be perfectly illustrated with some reflection measurements obtained by sensor B. These measurements are shown in table 13 and represent the average reflection measurements for the 5 classes of the E 50 testsets but for 3 producing countries that are present in the testset in sufficient large quantities: Table 14 Reflection measurements of sensor B for 3 producing countries Production S Production V Production X Super fit Fit Acceptable Unfit Super unfit This table shows that at least sensor B sees large differences in banknotes produced by different countries. Production V shows regardless of the soillevel always a significant higher reflection level then productions S and X. X is in all case is the darkest of the 3. If for instance the threshold is based on V notes for instance halfway the acceptable and the unfit level resulting in a threshold of 1251, it can be seen that with this threshold which might be suitable for V, we are already on the average value for fit X notes and in between the fits and acceptables of S. If on the other hand our threshold is based on production X and therefore the threshold halfway 1177 and 1092 would be chosen, resulting in a threshold of 1132, it is obvious that now all unfit V notes are allowed into circulation and presumably a good part of the super unfits of V as well. This is the core of our problem that is impossible to solve whichever perfect sensor we may develop. As long as the original product we have to sort shows these kinds of variations we can never reach a satisfying result, at least as long as we only rely on the optical properties that demonstrable show these variations.

19 For a discussion on the sources of these variations I refer to a presentation I have given at the Currency Conference in Bangkok, but in fig 13 we already get an idea of the background of this problem: Spectral reflection S,V and X production nm 390nm 420nm 450nm 480nm 510nm 540nm 570nm 600nm 630nm 660nm 690nm 720nm Fig 13 Spectral reflection for S, V and X production for E 50 production. X S V From the curves in this graph we see why the X notes show a totally different behaviour then the V notes: The X notes have a dip in the spectral reflection curve in the area where sensor B is the most sensitive. The V notes seem to be just above the average in this area. Although sensor B will have no problem with it, we may expect serious problems for the sensors that are sensitive in the red and infrared area with production S. We really should try to harmonize the production of new banknotes. How on earth are we as sorting specialists able to cope with these types of production variations? 7 Stability Until now we have only looked at the performance over a short period, but stability of a sensor is essential as well. To illustrate the effect of an unstable and a stable sensor I show you 2 curves representing the behaviour of the soil sensor before and after the introduction of the ring shaped LED illumination in sensor B. From the curves of fig 14 it can be seen that the unfit rate before modification showed a variation between below 10 % to as high as 45 %. Needless to say that although there may be slight variations in unfit rate due to quality differences, the variation that is shown here far exceeds the normal quality fluctuations of the circulation. After the introduction of the LED illumination this variation is reduced to a band that ranges between 26 and 40 % in my opinion still high, but far more acceptable then the variations that were observed while using the halogen illumination.

20 Daily unfit rate over 4 months before and after modification (euro 5) 0,5 0,4 0,3 0,2 0, Days Before After Fig 14 Stabilizing effect of introduction of ring shaped LED illumination A second form of stability is the reproducibility of a sensor. When presenting the same note several times to a sensor, a good sensor always should show the same reflection values. In fig 15 a sample of the reproducibility for sensor B is presented Fig 15 Reflectance measurement for 2 measurement sessions of testset E50 The first time the notes were measured on 30 October. Then these notes have been sorted in the different sorting machines (A H) as described before. On 27 January the same notes were sorted again after an experiment with sensor B. During this experiment the LED illumination was temporarily replaced with the previous halogen illumination. After the

21 experiment the halogen lamp was replaced again with the LED illumination and the same notes were transported as the 30 th of october. A sample of the result of the 2 measurements is shown in fig 12 and is in my opinion striking. For the 4 denominations we observed correlation factors between 0,995 and 0,999. It means that the sensor is always measuring the same value and that the influence of transport variations is negligible. It would be interesting to repeat these measurements for the other sensors as well to obtain these curves for all measured sensors. 8 Potential improvements In the previous chapter I have expressed my view that we have reached the limit of the pure optical methods to determine soiling level. I am not expecting a breakthrough if we further follow the current trend of higher resolution camera s or more image processing power. The only thing that will be achieved is that the production differences can be recognized with ever higher precision. At best a performance level may be expected that is similar to sensor B. Of course we can look for totally different properties then the optical properties we used so far, sound and ultrasound for instance and I have also heard rumors of X-ray technology, but these technologies must still prove their potential power. Nevertheless I see 2 options for improvement that are worthwhile a further study: 7.1 Producer information The first potential improvement that could be considered is to add production information to the soil decision. In the example on production variations it was demonstrated that during the lifecycle of euro 50 notes, the X notes continuously showed a lower reflection value then the S notes while the V notes always showed a much higher reflection then both S and X. This knowledge can be used when a soil decision is made. So when sorting a X production note, a certain value could be added to the measured value and then apply a general threshold, or a specific threshold for each individual production might be introduced. Applying such a technique to the results of E50 indeed gives a hopeful result. Looking at the filter function that was applied to obtain the comparison in performance between various detectors, it was found in tests that the amount of visually fit notes that must be destroyed to fulfill the criterion can be reduced from 21 % to 14.5 % using this technique of production recognition. An easy way to do this is by using an OCR serial number reader. Looking at the value of the destroyed fit notes a reduction from 13.3 to 7.7 was observed using this technique. Per million sorted notes this would mean that the loss of premature destroyed notes could be reduced from 1950 to 1050 per million sorted notes in the case of sensor B. Could such a technique be realized in practice? At DNB this way of adding producer information to the soil detection can be implemented quite easily as the main requirement for this option is already fulfilled: we read serial numbers and we know it is not a big effort to implement such an algorithm in the current sorting machine. The investment is very low compared with the potential gains. But there is an other prerequisite in order to implement this option; it is necessary to have the production data as we have to know what the offset is for each production. Again for DNB is this problem can be solved, as we have registered all data of all sorted notes since the

22 introduction of the euro. So with a moderate effort counted in the order of man months, it should be possible to find the relevant offsets for all euro productions with a reasonable precision. However would such an investment be worthwhile if you do not have this infrastructure? Although I do not think that high resolution cameras that we see appearing in sorting machines nowadays, will have much influence on the performance of the soil sensors, one thing these cameras nowadays can do is reading the serial numbers. Applying the technique of adding the producer s information to the soil sensor in principle can be quite easy implemented with this new generation of sensors. 7.2 Spectral information It has been demonstrated that soil is predominantly visible in the blue part of the spectrum and it has been shown that soil sensors that use this part of the spectrum perform better but still not satisfying. We found huge differences between productions, but if we look in more detail within the different productions we will also see variations that cannot be ignored. Spectral reflection 2 X notes X X nm 390nm 420nm 450nm 480nm 510nm 540nm 570nm 600nm 630nm 660nm 690nm 720nm Fig 16 Spectral reflection for 2 notes of same production Fig 16 shows what can be expect as variation within a production. Sensor B will see this difference in spectral reflection curve as a difference of 120 points or more then the difference of a full class. It is obvious that we cannot get the production information for each individual note so the potential solution presented in the previous chapter cannot be implemented on a per note basis. Although the reflection values may vary significantly also within a production it seems that the shape of the curve is more or less stable within a production. This shape is presumably defined by the various pigments and fillers that are added to the paper during the production process and seems to be rather stable during the production. So a possible solution is to base the soil decision on the shape of the curve instead of using the absolute values. Although this principle has not yet been tested, I expect that soil detection on this principle can have a positive effect. 8 Conclusions 8.1 For euro banknotes the best option is a reflection measurement in the blue part of the visual spectrum, more precise around 450 nm.

23 8.2 The difference in performance between the best performing sensors and the poor performing sensors can be attributed to the part of the spectrum the sensors use for their decision. 8.3 Illumination and depth of field also play a role in the performance, especially a double sided or ring shaped illumination will improve sorting quality significantly. 8.4 Camera solutions show no advantage over a simple photocell solution, possibly a camera has a negative effect. 8.5 The cost of a poor performing sensor is estimated as 6000 euro per million sorted banknotes, which is approximately 4000 euro more then the best performing sensor. Given an annual capacity of 200 million notes, even replacing a poor performing sorting machine could be an economically justifiable option. 8.6 Even the best performing sensor cannot perform better due to the large variation of optical and spectral properties of the different paper productions. Stabilizing these productions is essential for better sorting performance as are improved sensors. 8.7 Potentially there are solutions that can result in further increased performance, however these solutions need a kind of production recognition and will require the use of the spectral properties of the banknote.