A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System

Tsung Han Tsai; Po Ting Chi; Kuo Hsing Cheng

doi:10.1109/DDECS.2019.8724656

A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Since 2011, due to the growth in the amount of information, the innovation of learning algorithms and the improvement of computer technology make the application of artificial intelligence feasible in a wide range of fields. This paper presents a sketch classifier technique with deep learning models. We use the depth-wise convolution layer to lighten the deep neural network. The result shows the improvement in approximately 1/5 of computation. We use Google Quick Draw dataset to train and evaluate the network, which can have 98% accuracy in 10 categories and 85% accuracy in 100 categories. Finally, we realize it on STM32F469I Discovery development board for demonstration. The system can achieve real-time implementation of sketch classification.

Original language	English
Title of host publication	Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728100739
DOIs	https://doi.org/10.1109/DDECS.2019.8724656
State	Published - Apr 2019
Event	22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019 - Cluj-Napoca, Romania Duration: 24 Apr 2019 → 26 Apr 2019

Publication series

Name	Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019

Conference

Conference	22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019
Country/Territory	Romania
City	Cluj-Napoca
Period	24/04/19 → 26/04/19

Keywords

Deep Learning
Embedded System
Neural Network
Sketch Classification

Access to Document

10.1109/DDECS.2019.8724656

Cite this

Tsai, T. H., Chi, P. T., & Cheng, K. H. (2019). A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System. In Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019 Article 8724656 (Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDECS.2019.8724656

Tsai, Tsung Han ; Chi, Po Ting ; Cheng, Kuo Hsing. / A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System. Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019).

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title = "A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System",

abstract = "Since 2011, due to the growth in the amount of information, the innovation of learning algorithms and the improvement of computer technology make the application of artificial intelligence feasible in a wide range of fields. This paper presents a sketch classifier technique with deep learning models. We use the depth-wise convolution layer to lighten the deep neural network. The result shows the improvement in approximately 1/5 of computation. We use Google Quick Draw dataset to train and evaluate the network, which can have 98\% accuracy in 10 categories and 85\% accuracy in 100 categories. Finally, we realize it on STM32F469I Discovery development board for demonstration. The system can achieve real-time implementation of sketch classification.",

keywords = "Deep Learning, Embedded System, Neural Network, Sketch Classification",

author = "Tsai, \{Tsung Han\} and Chi, \{Po Ting\} and Cheng, \{Kuo Hsing\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019 ; Conference date: 24-04-2019 Through 26-04-2019",

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language = "???core.languages.en\_GB???",

series = "Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019",

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Tsai, TH, Chi, PT & Cheng, KH 2019, A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System. in Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019., 8724656, Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019, Institute of Electrical and Electronics Engineers Inc., 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019, Cluj-Napoca, Romania, 24/04/19. https://doi.org/10.1109/DDECS.2019.8724656

A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System. / Tsai, Tsung Han; Chi, Po Ting; Cheng, Kuo Hsing.
Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8724656 (Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System

AU - Tsai, Tsung Han

AU - Chi, Po Ting

AU - Cheng, Kuo Hsing

PY - 2019/4

Y1 - 2019/4

N2 - Since 2011, due to the growth in the amount of information, the innovation of learning algorithms and the improvement of computer technology make the application of artificial intelligence feasible in a wide range of fields. This paper presents a sketch classifier technique with deep learning models. We use the depth-wise convolution layer to lighten the deep neural network. The result shows the improvement in approximately 1/5 of computation. We use Google Quick Draw dataset to train and evaluate the network, which can have 98% accuracy in 10 categories and 85% accuracy in 100 categories. Finally, we realize it on STM32F469I Discovery development board for demonstration. The system can achieve real-time implementation of sketch classification.

AB - Since 2011, due to the growth in the amount of information, the innovation of learning algorithms and the improvement of computer technology make the application of artificial intelligence feasible in a wide range of fields. This paper presents a sketch classifier technique with deep learning models. We use the depth-wise convolution layer to lighten the deep neural network. The result shows the improvement in approximately 1/5 of computation. We use Google Quick Draw dataset to train and evaluate the network, which can have 98% accuracy in 10 categories and 85% accuracy in 100 categories. Finally, we realize it on STM32F469I Discovery development board for demonstration. The system can achieve real-time implementation of sketch classification.

KW - Deep Learning

KW - Embedded System

KW - Neural Network

KW - Sketch Classification

UR - https://www.scopus.com/pages/publications/85067548906

U2 - 10.1109/DDECS.2019.8724656

DO - 10.1109/DDECS.2019.8724656

M3 - 會議論文篇章

AN - SCOPUS:85067548906

T3 - Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019

BT - Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019

Y2 - 24 April 2019 through 26 April 2019

ER -

Tsai TH, Chi PT, Cheng KH. A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System. In Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8724656. (Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019). doi: 10.1109/DDECS.2019.8724656

A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System

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