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Notice of retraction
Vol. 34, No. 8(3), S&M3042

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Sensors and Materials, Volume 33, Number 9(1) (2021)
Copyright(C) MYU K.K.
pp. 3037-3052
S&M2672 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2021.3241
Published: September 10, 2021

Drug Verification System Based on Deep Learning Multiscale Rotating Rectangle Detector and Feature Embedding [PDF]

Shih-Pang Tseng, Che-Wen Chen, Wei-Yan Jang, and Jhing-Fa Wang

(Received December 30, 2020; Accepted August 13, 2021)

Keywords: drug detection, deep residual network, feature pyramid network, image feature embedded network, medication safety

This research is aimed at the development of automatic drug image verification functions. Our verification system is composed of two stages. The first stage is an arbitrarily axis-aligned object detector, which is mainly based on a deep residual network and feature pyramid network (FPN). The detector predicts the rotation bounding boxes for drugs using multiscale feature maps generated by the FPN. Then, the rotation bounding boxes are axially aligned, and the drug image is cropped according to the axis-aligned bounding box. The second stage is a feature matcher, which is based on a feature embedding network. The embedding feature extracted by the feature embedding network is combined with the geometric feature obtained by the arbitrarily axis-aligned object detector to determine whether the drug in the image belongs to the category specified by the user. The database used in this research is an image database created by imaging drugs provided by domestic local medical centers. Our verification system achieved a false positive rate (FPR) of 0.047% in verification tasks of drugs of 21 categories.

Corresponding author: Shih-Pang Tseng


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Shih-Pang Tseng, Che-Wen Chen, Wei-Yan Jang, and Jhing-Fa Wang, Drug Verification System Based on Deep Learning Multiscale Rotating Rectangle Detector and Feature Embedding, Sens. Mater., Vol. 33, No. 9, 2021, p. 3037-3052.



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