Detection method for paper money splicing signal

Abstract

The invention provides a detection method for paper money splicing signals. The method comprises the following steps: positioning a detection region of paper money, acquiring image pixel of the detection region; obtaining coverage lines of the detection region; obtaining a pixel gray distribution diagram of the detection region; obtaining and analyzing characteristics of all coverage lines, finding out a line whose black signal and white signal are most obvious, marking the line; and presetting an experience difference value threshold of a genuine note, comparing with a detection result, to obtain a conclusion. Through circumferential coverage, the method can accurately position white abnormal lines and black abnormal lines, and whether a signal is a splicing signal is determined by the characteristic difference of the white abnormal lines and black abnormal lines. The method does not need to process binaryzation and hough transformation, so time complexity is greatly reduced. Since the binaryzation and the hough detection need to calculate each point of an image, but the method just needs to calculate the lines in characteristic number, algorithmic complexity is reduced, identification time of splicing paper money is reduced, and paper money identification efficiency is improved.

Description

technical field [0001] The invention relates to a banknote detection method, in particular to a banknote splicing signal detection method. Background technique [0002] At present, there are a large number of spliced ​​banknotes circulating in the market. The so-called spliced ​​banknotes are real banknotes spliced ​​with real banknotes, counterfeit banknotes spliced ​​with real banknotes, and counterfeit banknotes spliced ​​with counterfeit banknotes. Splicing banknotes is a difficult point in banknote inspection and identification, especially for real banknotes splicing real banknotes. In the prior art, the detection method for banknotes is to binarize all pixels of the scanned image of banknotes, and then perform hough transformation (hough detection is to detect straight lines at the splicing place, which is a general method for image processing), in Hough spaces detect anomalous linear signals. However, the time complexity of the algorithm is extremely high, and the b...

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Problems solved by technology

However, the time complexity of the algorithm is extremely high, and the binarized noise points will have an impact on the parameters of the hough space, especially the points in the two spaces of the hough transform are not one-to-one correspondence, which increases the complexity of the algorithm, because it is necessary to put The data after the hough transformation is mapped to the image data, which will be time-consuming

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