Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Accurate stacked-sheet counting method based on deep learning

Not Accessible

Your library or personal account may give you access

Abstract

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.

© 2020 Optical Society of America

Full Article  |  PDF Article
More Like This
Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy

Chen Li, Adele Moatti, Xuying Zhang, H. Troy Ghashghaei, and Alon Greenbaum
Biomed. Opt. Express 12(8) 5214-5226 (2021)

Robust and accurate sub-pixel extraction method of laser stripes in complex circumstances

Maosen Wan, Shuaidong Wang, Huining Zhao, Huakun Jia, and Liandong Yu
Appl. Opt. 60(36) 11196-11204 (2021)

Learning Siamese networks for laser vision seam tracking

Yanbiao Zou, Jinchao Li, Xiangzhi Chen, and Rui Lan
J. Opt. Soc. Am. A 35(11) 1805-1813 (2018)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (12)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (3)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (8)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved