Research of Automobile Fault Lamp Recognition Algorithm Based on Mobile Platform

Jiawei Wang; Jinyi Li; Chongwen Wang

doi:10.1145/3383972.3384030

Research of Automobile Fault Lamp Recognition Algorithm Based on Mobile Platform

Jiawei Wang, Jinyi Li, Chongwen Wang

School of Computer Science and Technology

Beijing Institute of Technology

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

1 Citation (Scopus)

Abstract

In order to identify dashboard fault indicators timely and help drivers take countermeasures, we design and implement a mobile platform-based dashboard fault indicator recognition algorithm. First we collect dashboard fault indicator images through mobile phone shooting and network download, and then use image augmentation to expand our dataset. This paper improves the SSD algorithm by increasing the number of feature maps in the feature pyramid and changing the specifications of the default box. These changes made the algorithm better detect fault indicators. Aiming at the excessive volume in model compression and transplantation, we select a better compression algorithm by comparing three different compression methods. Finally, the compressed model is transplanted to the mobile terminal.

Original language	English
Title of host publication	Proceedings of the 2020 12th International Conference on Machine Learning and Computing, ICMLC 2020
Publisher	Association for Computing Machinery
Pages	318-323
Number of pages	6
ISBN (Electronic)	9781450376426
DOIs	https://doi.org/10.1145/3383972.3384030
Publication status	Published - 15 Feb 2020
Event	12th International Conference on Machine Learning and Computing, ICMLC 2020 - Shenzhen, China Duration: 15 Feb 2020 → 17 Feb 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	12th International Conference on Machine Learning and Computing, ICMLC 2020
Country/Territory	China
City	Shenzhen
Period	15/02/20 → 17/02/20

Keywords

Dashboard fault indicator
SSD algorithm; model compression

Access to Document

10.1145/3383972.3384030

Cite this

@inproceedings{46545ccfd9b54c1eba0eece9c0f7f427,

title = "Research of Automobile Fault Lamp Recognition Algorithm Based on Mobile Platform",

abstract = "In order to identify dashboard fault indicators timely and help drivers take countermeasures, we design and implement a mobile platform-based dashboard fault indicator recognition algorithm. First we collect dashboard fault indicator images through mobile phone shooting and network download, and then use image augmentation to expand our dataset. This paper improves the SSD algorithm by increasing the number of feature maps in the feature pyramid and changing the specifications of the default box. These changes made the algorithm better detect fault indicators. Aiming at the excessive volume in model compression and transplantation, we select a better compression algorithm by comparing three different compression methods. Finally, the compressed model is transplanted to the mobile terminal.",

keywords = "Dashboard fault indicator, SSD algorithm; model compression",

author = "Jiawei Wang and Jinyi Li and Chongwen Wang",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 12th International Conference on Machine Learning and Computing, ICMLC 2020 ; Conference date: 15-02-2020 Through 17-02-2020",

year = "2020",

month = feb,

day = "15",

doi = "10.1145/3383972.3384030",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "318--323",

booktitle = "Proceedings of the 2020 12th International Conference on Machine Learning and Computing, ICMLC 2020",

}

Wang, J, Li, J & Wang, C 2020, Research of Automobile Fault Lamp Recognition Algorithm Based on Mobile Platform. in Proceedings of the 2020 12th International Conference on Machine Learning and Computing, ICMLC 2020. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 318-323, 12th International Conference on Machine Learning and Computing, ICMLC 2020, Shenzhen, China, 15/02/20. https://doi.org/10.1145/3383972.3384030

Research of Automobile Fault Lamp Recognition Algorithm Based on Mobile Platform. / Wang, Jiawei; Li, Jinyi; Wang, Chongwen.
Proceedings of the 2020 12th International Conference on Machine Learning and Computing, ICMLC 2020. Association for Computing Machinery, 2020. p. 318-323 (ACM International Conference Proceeding Series).

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

TY - GEN

T1 - Research of Automobile Fault Lamp Recognition Algorithm Based on Mobile Platform

AU - Wang, Jiawei

AU - Li, Jinyi

AU - Wang, Chongwen

PY - 2020/2/15

Y1 - 2020/2/15

N2 - In order to identify dashboard fault indicators timely and help drivers take countermeasures, we design and implement a mobile platform-based dashboard fault indicator recognition algorithm. First we collect dashboard fault indicator images through mobile phone shooting and network download, and then use image augmentation to expand our dataset. This paper improves the SSD algorithm by increasing the number of feature maps in the feature pyramid and changing the specifications of the default box. These changes made the algorithm better detect fault indicators. Aiming at the excessive volume in model compression and transplantation, we select a better compression algorithm by comparing three different compression methods. Finally, the compressed model is transplanted to the mobile terminal.

AB - In order to identify dashboard fault indicators timely and help drivers take countermeasures, we design and implement a mobile platform-based dashboard fault indicator recognition algorithm. First we collect dashboard fault indicator images through mobile phone shooting and network download, and then use image augmentation to expand our dataset. This paper improves the SSD algorithm by increasing the number of feature maps in the feature pyramid and changing the specifications of the default box. These changes made the algorithm better detect fault indicators. Aiming at the excessive volume in model compression and transplantation, we select a better compression algorithm by comparing three different compression methods. Finally, the compressed model is transplanted to the mobile terminal.

KW - Dashboard fault indicator

KW - SSD algorithm; model compression

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U2 - 10.1145/3383972.3384030

DO - 10.1145/3383972.3384030

M3 - Conference contribution

AN - SCOPUS:85085931492

T3 - ACM International Conference Proceeding Series

SP - 318

EP - 323

BT - Proceedings of the 2020 12th International Conference on Machine Learning and Computing, ICMLC 2020

PB - Association for Computing Machinery

T2 - 12th International Conference on Machine Learning and Computing, ICMLC 2020

Y2 - 15 February 2020 through 17 February 2020

ER -

Research of Automobile Fault Lamp Recognition Algorithm Based on Mobile Platform

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