Application of Capsule Network to Tablet Identification
Authors: 
Wenliang He, Minling ZhuAuthors Info & Claims
Wenliang He, Minling ZhuAuthors Info & ClaimsArticle No.: 23, Pages 1 - 6
Abstract
To address the problem of pill defect recognition, we propose to apply capsule neural network for pill defect recognition. The effects of dynamic routing iteration rounds and different compression function constant term schemes on the performance of the capsule neural network are investigated in the context of small data sets, and the model effects are verified experimentally. The experimental results show that both the number of dynamic routing iteration rounds and different compression function constant term schemes can affect the performance of the capsule neural network, and it is essential to choose an appropriate scheme in the process of training the model. The capsule neural network is found to be suitable for the field of pill defect recognition through experiments.
CCS CONCEPTS • Computing methodologies∼Visual content-based indexing
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Published In
December 2021
699 pages
ISBN:9781450385053
DOI:10.1145/3508546
Copyright © 2021 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 February 2022
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- Research-article
- Research
- Refereed limited
Funding Sources
- National Natural Science Foundation of China Grant Program
- National Ministry of Science and Technology Key R&D Program
- Artificial Intelligence and Independent Innovation Talent Cultivation
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ACAI'21
ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence
December 22 - 24, 2021
Sanya, China
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Overall Acceptance Rate 173 of 395 submissions, 44%
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