Batch counting of thin sheet products such as paper has been widely applied in the industrial field. To solve the problem of machine vision quantity measurement for very thin paper stacks

a robust image counting algorithm based on global period constraint and local pattern correlation is presented. 1D profiles were extracted along the direction of stack height

and then denoised with Fourier spectrum analysis and a comb filter to preserve the useful period signal. Each candidate paper was located using a traditional peak finding algorithm. An optimal peak template was constructed

and an improved function of normalized cross-correlation was presented to calculate the correlation coefficient between the former template and the original signal by local matching. This approach helped reduce false detection from complex factors such as rugged edge

varying thickness and gap

and irregular arrangement. The collinear property and similar shape of signal wave were utilized to further suppress clutter

and the ultimate measure was obtained from optimal statistics of different profile counts. For comparison

the proposed method and several traditional algorithms were used together in counting experiments with different types of paper sheets with thickness varying from 0.08 mm to 0.23 mm. Our algorithm was verified to eliminate interference more effectively than other methods

and the missing and false alarm rates appeared comparatively low. Our algorithm can achieve very high detection accuracy for paper sheets with thickness of more than 0.08 mm and has good real-time performance

which makes it suitable for in-line industrial applications with high accuracy requirement.