Year detection and recognition of euro coin
GUO Xuefeng①②,CHEN Honglei①②,ZHANG Dongbo ①②(①The College of Information Engineering,Xiangtan University)
Objective In the circulation process, the appearance quality of coins decreases due to wear, so it is necessary to recycle the badly worn appearance. It is a common practice to judge whether the coins should be recycled by evaluating the appearance quality. The year of coins is an important information to judge the appearance quality of coins. In order to accurately identify the euro coins in circulation, it is necessary to detect and identify the year of issue on the euro coins. However, due to the uncertainty of the position and posture of the Euro coin number, the non-normalization of the size, the interference of other characters and the diversity of the number arrangement, it is difficult to realize the automatic detection, recognition and interpretation of the Euro coin year by using computer vision algorithm. Method The method of detecting and recognizing euro coin year in this paper consists of two steps: first, we use Faster-RCNN to detect the number. The model algorithm is mainly completed in four steps: the first step is to send the whole image to be detected into the convolution neural network to get the convolution feature map; the second step is to input the feature map into the RPN network to get multiple candidate regions of the target; the third step is to use the ROI pooling layer to extract the features of the candidate regions; the fourth step is to use the multi-task classifier to carry out position regression to get the precise position coordinates of the target. A self-built experimental platform was used to collect five large coins from 12 EU countries. The five currencies were 2 euros, 1 euro, 50 euro cents, 20 euro cents and 10 euro cents respectively. In the process of collecting, the coins were rotated at small angles continuously, and the coins were captured at various angles as far as possible. A total of 4429 pictures were collected from different angles; There are four ways to interpret the ranking order of the number of coin years. For a given coin image, which sort of method to interpret the year is the first clear question. According to observation, the year arrangement of a certain currency value in a country is fixed. If we can predetermine the currency value and country of a coin, the corresponding year interpretation rules can be determined. This is a problem that can be solved because the image size of coins with different values varies significantly and the coin patterns of different countries are different. Therefore, the second step is: by using K-means clustering algorithm, the obtained digital candidate boxes can be grouped into 4 categories, and the most confident candidate boxes are selected in each category, finally, according to the pre-determined year arrangement pattern of different country coins, the correct year information can be obtained by proper sorting algorithm. Result On a self-built experimental platform, 4429 pictures were collected from 5 kinds of coins with large currency value in 12 EU countries. The training samples and test samples were divided according to the ratio of 1:1. The experimental results show that the detection accuracy of the method is 89.62%, and the calculation time is about 215ms, which basically meets the requirements of accuracy and real-time. Conclusion The algorithm has good performance of real-time, robustness and high precision, and has high practical application value. Although the detection accuracy of existing algorithms is close to 90%, there is still much room for improvement, which can be considered from two aspects to solve the existing two error situations. One is to improve the clustering algorithm to achieve compact clustering or clustering in accordance with the law of year number distribution, which can eliminate the misdetection of characters or symbols to a certain extent; the other is to further improve the Faster-RCNN network model and the simplified processing algorithm of candidate boxes to improve the detection accuracy of closely arranged digital boxes.