Laminated sheet detection plays an extreme role in the manufacturing field of sheet products such as paper, cigarette package, packing box, PCB and so on.The precision of this processing directly affects the economic benefit of the factory and the subsequent production operation. However, the major abtacles in this term such as the thickness of paper, material, inhomogeneous illumination, density of noises still challenge the recent approaches. To overcome these problems, the method which combines minimum curvature and peak detection is presented. First, the profiles of the stacked papers along the vertical orientation of the paper are extracted. The curvatures of the profiles then are calculated after applying Gaussian filter. Later, the central lines of the stacked papers need to be detected. The width of the region where the curvature is negative represents the thickness of the paper. Afterward, the positions of stacked papers can be corrected by judging the distance between the two adjacent center points and the gray features. Finally, the counting result can be acquired by performing peak detection on the ridge image. Our algorithm can accurately detect the abnormal paper by fusing the gray features of the stacked papers and the distance of adjacent paper. Experimental results show that the error rate of our method is less than 0.01% for the paper with the thickness between 0.05mm and 0.2mm.