The accurate counting of laminated sheets, such as packing or printing sheets in industry, is extremely important because it greatly affects the economic cost. However, the different thicknesses, adhesion properties, and breakage points and the low contrast of sheets remain challenges to traditional counting methods based on image processing. This paper proposes a new stacked-sheet counting method with a deep learning approach using the U-Net architecture. A specific dataset according to the characteristics of stack side images is collected. The stripe of the center line of each sheet is used for semantic segmentation, and the complete side images of the slices are segmented via training with small image patches and testing with original large images. With this model, each pixel is classified by multi-layer convolution and deconvolution to determine whether it is the target object to be detected. After the model is trained, the test set is used to test the model, and a center region segmentation map based on the pixel points is obtained. By calculating the statistical median value of centerline points across different sections in these segmented images, the number of sheets can be obtained. Compared with traditional image algorithms in real product counting experiments, the proposed method can achieve better performance with higher accuracy and a lower error rate.

中文翻译：

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基于深度学习的精确堆叠纸计数方法。

精确计数层压板，例如工业中的包装或印刷板，非常重要，因为它会极大地影响经济成本。然而，不同的厚度，粘合性能，断裂点和低对比度的纸张仍然是基于图像处理的传统计数方法的挑战。本文提出了一种新的堆叠纸计数方法，该方法采用了基于U-Net架构的深度学习方法。根据堆栈侧面图像的特征收集特定的数据集。每张纸的中心线的条纹用于语义分割，并且通过使用小图像块进行训练并使用原始大图像进行测试来对切片的完整侧面图像进行分割。有了这个模型，通过多层卷积和反卷积对每个像素进行分类，以确定它是否是要检测的目标对象。对模型进行训练后，使用测试集对模型进行测试，并获得基于像素点的中心区域分割图。通过计算这些分割图像中不同区域的中心线点的统计中值，可以获得张数。与实际产品计数实验中的传统图像算法相比，该方法具有更好的性能，更高的准确度和更低的错误率。通过计算这些分割图像中不同区域的中心线点的统计中值，可以获得张数。与实际产品计数实验中的传统图像算法相比，该方法具有更好的性能，更高的准确度和更低的错误率。通过计算这些分割图像中不同区域的中心线点的统计中值，可以获得张数。与实际产品计数实验中的传统图像算法相比，该方法具有更好的性能，更高的准确度和更低的错误率。