An Modified YOLOv5 Algorithm to Improved Image Identification for Autonomous Driving

Chun Chieh Wang; Yi Shun Lu; Wen Piao Lin

doi:10.1109/CSCE60160.2023.00442

An Modified YOLOv5 Algorithm to Improved Image Identification for Autonomous Driving

Chun Chieh Wang, Yi Shun Lu, Wen Piao Lin

電機工程學系(含學碩博士班及碩士在職專班)

Chang Gung University

研究成果: 圖書/報告稿件的類型 › 會議稿件 › 同行評審

摘要

In this paper we propose a solution to improve image identification on the early expressway engineering warning signs for autonomous driving. This scheme uses the modified lightweight YOLOv5 algorithm model to train feature categories. After manual labeling and data augmentation, the dataset is sent to the advance deep neural network for training. The practical experimental results show that the modified algorithm model can effectively identify engineering warning signs on the expressway. It allows drivers and construction units on the expressway to use the road more safely.

原文	英語
主出版物標題	Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	2722-2724
頁數	3
ISBN（電子）	9798350327595
DOIs	https://doi.org/10.1109/CSCE60160.2023.00442
出版狀態	已出版 - 2023
事件	2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 - Las Vegas, 美國持續時間: 24 07 2023 → 27 07 2023

出版系列

名字	Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

Conference

Conference	2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
國家/地區	美國
城市	Las Vegas
期間	24/07/23 → 27/07/23

文獻附註

Publisher Copyright:
© 2023 IEEE.

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10.1109/CSCE60160.2023.00442

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Link to publication in Scopus

引用此

Wang, C. C., Lu, Y. S., & Lin, W. P. (2023). An Modified YOLOv5 Algorithm to Improved Image Identification for Autonomous Driving. 於 Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 (頁 2722-2724). (Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCE60160.2023.00442

Wang, Chun Chieh ; Lu, Yi Shun ; Lin, Wen Piao. / An Modified YOLOv5 Algorithm to Improved Image Identification for Autonomous Driving. Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. 頁 2722-2724 (Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023).

@inproceedings{2f213dd44ce84d6d8a6928aeefb40e8a,

title = "An Modified YOLOv5 Algorithm to Improved Image Identification for Autonomous Driving",

abstract = "In this paper we propose a solution to improve image identification on the early expressway engineering warning signs for autonomous driving. This scheme uses the modified lightweight YOLOv5 algorithm model to train feature categories. After manual labeling and data augmentation, the dataset is sent to the advance deep neural network for training. The practical experimental results show that the modified algorithm model can effectively identify engineering warning signs on the expressway. It allows drivers and construction units on the expressway to use the road more safely.",

keywords = "autonomous driving, data augmentation, deep neural network, identification, YOLOv5 algorithm",

author = "Wang, {Chun Chieh} and Lu, {Yi Shun} and Lin, {Wen Piao}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 ; Conference date: 24-07-2023 Through 27-07-2023",

year = "2023",

doi = "10.1109/CSCE60160.2023.00442",

language = "英语",

series = "Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2722--2724",

booktitle = "Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023",

address = "美国",

}

Wang, CC, Lu, YS & Lin, WP 2023, An Modified YOLOv5 Algorithm to Improved Image Identification for Autonomous Driving. 於 Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023. Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023, Institute of Electrical and Electronics Engineers Inc., 頁 2722-2724, 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023, Las Vegas, 美國, 24/07/23. https://doi.org/10.1109/CSCE60160.2023.00442

An Modified YOLOv5 Algorithm to Improved Image Identification for Autonomous Driving. / Wang, Chun Chieh; Lu, Yi Shun; Lin, Wen Piao.
Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 2722-2724 (Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023).

研究成果: 圖書/報告稿件的類型 › 會議稿件 › 同行評審

TY - GEN

T1 - An Modified YOLOv5 Algorithm to Improved Image Identification for Autonomous Driving

AU - Wang, Chun Chieh

AU - Lu, Yi Shun

AU - Lin, Wen Piao

PY - 2023

Y1 - 2023

N2 - In this paper we propose a solution to improve image identification on the early expressway engineering warning signs for autonomous driving. This scheme uses the modified lightweight YOLOv5 algorithm model to train feature categories. After manual labeling and data augmentation, the dataset is sent to the advance deep neural network for training. The practical experimental results show that the modified algorithm model can effectively identify engineering warning signs on the expressway. It allows drivers and construction units on the expressway to use the road more safely.

AB - In this paper we propose a solution to improve image identification on the early expressway engineering warning signs for autonomous driving. This scheme uses the modified lightweight YOLOv5 algorithm model to train feature categories. After manual labeling and data augmentation, the dataset is sent to the advance deep neural network for training. The practical experimental results show that the modified algorithm model can effectively identify engineering warning signs on the expressway. It allows drivers and construction units on the expressway to use the road more safely.

KW - autonomous driving

KW - data augmentation

KW - deep neural network

KW - identification

KW - YOLOv5 algorithm

UR - http://www.scopus.com/inward/record.url?scp=85191149502&partnerID=8YFLogxK

U2 - 10.1109/CSCE60160.2023.00442

DO - 10.1109/CSCE60160.2023.00442

M3 - 会议稿件

AN - SCOPUS:85191149502

T3 - Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

SP - 2722

EP - 2724

BT - Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

Y2 - 24 July 2023 through 27 July 2023

ER -

Wang CC, Lu YS, Lin WP. An Modified YOLOv5 Algorithm to Improved Image Identification for Autonomous Driving. 於 Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 2722-2724. (Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023). doi: 10.1109/CSCE60160.2023.00442

An Modified YOLOv5 Algorithm to Improved Image Identification for Autonomous Driving

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