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

Department of Electrical Engineering

Chang Gung University

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

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.

Original language	English
Title of host publication	Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2722-2724
Number of pages	3
ISBN (Electronic)	9798350327595
DOIs	https://doi.org/10.1109/CSCE60160.2023.00442
State	Published - 2023
Event	2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 - Las Vegas, United States Duration: 24 07 2023 → 27 07 2023

Publication series

Name	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
Country/Territory	United States
City	Las Vegas
Period	24/07/23 → 27/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

autonomous driving
data augmentation
deep neural network
identification
YOLOv5 algorithm

Access to Document

10.1109/CSCE60160.2023.00442

Cite this

Wang, C. C., Lu, Y. S., & Lin, W. P. (2023). An Modified YOLOv5 Algorithm to Improved Image Identification for Autonomous Driving. In Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 (pp. 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. pp. 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. in 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., pp. 2722-2724, 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023, Las Vegas, United States, 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).

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

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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

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Wang CC, Lu YS, Lin WP. An Modified YOLOv5 Algorithm to Improved Image Identification for Autonomous Driving. In 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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Keywords

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