Road Signage and Road Obstacle Detection Using Deep Learning Method

Lee Cheng Juen; Ismail Mohd Khairuddin; Anwar P.P.Abdul Majeed; Muhammad Amirul Abdullah; Ahmad Fakhri Ab Nasir

doi:10.1007/978-981-99-8498-5_2

Road Signage and Road Obstacle Detection Using Deep Learning Method

Lee Cheng Juen
, Ismail Mohd Khairuddin^*
, Anwar P.P.Abdul Majeed
, Muhammad Amirul Abdullah
, Ahmad Fakhri Ab Nasir

^*Corresponding author for this work

School of Robotics

Universiti Malaysia Pahang Al-Sultan Abdullah

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

Abstract

This study presents a deep learning approach for road signage and road obstacle detection. The purpose of this research was to train a robust and efficient method for detecting road signs and obstacles in real time. This study aims to address the challenges and feasibility of deep learning on road signage and obstacles. A model is trained on YOLOv5 using transfer learning method and the performance of the proposed model was evaluated on a test set. The results showed the YOLOv5 achieved 93.5% mean average precision (mAP). The study concludes that deep learning is a promising method for road signage and road obstacle detection and has potential applications in the field of autonomous vehicles.

Original language	English
Title of host publication	Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
Editors	Andrew Tan, Fan Zhu, Haochuan Jiang, Kazi Mostafa, Eng Hwa Yap, Leo Chen, Lillian J. A. Olule, Hyun Myung
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	15-25
Number of pages	11
ISBN (Print)	9789819984978
DOIs	https://doi.org/10.1007/978-981-99-8498-5_2
Publication status	Published - 2024
Event	International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023 - Suzhou, China Duration: 22 Aug 2023 → 23 Aug 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	845
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023
Country/Territory	China
City	Suzhou
Period	22/08/23 → 23/08/23

Keywords

Deep learning
Object detection
YOLOv5

Access to Document

10.1007/978-981-99-8498-5_2

Cite this

Juen, L. C., Khairuddin, I. M., Majeed, A. P. P. A., Abdullah, M. A., & Nasir, A. F. A. (2024). Road Signage and Road Obstacle Detection Using Deep Learning Method. In A. Tan, F. Zhu, H. Jiang, K. Mostafa, E. H. Yap, L. Chen, L. J. A. Olule, & H. Myung (Eds.), Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023 (pp. 15-25). (Lecture Notes in Networks and Systems; Vol. 845). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8498-5_2

Juen, Lee Cheng ; Khairuddin, Ismail Mohd ; Majeed, Anwar P.P.Abdul et al. / Road Signage and Road Obstacle Detection Using Deep Learning Method. Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. editor / Andrew Tan ; Fan Zhu ; Haochuan Jiang ; Kazi Mostafa ; Eng Hwa Yap ; Leo Chen ; Lillian J. A. Olule ; Hyun Myung. Springer Science and Business Media Deutschland GmbH, 2024. pp. 15-25 (Lecture Notes in Networks and Systems).

@inproceedings{0b4b9298f4e746fd93b3e1438b5ac8aa,

title = "Road Signage and Road Obstacle Detection Using Deep Learning Method",

abstract = "This study presents a deep learning approach for road signage and road obstacle detection. The purpose of this research was to train a robust and efficient method for detecting road signs and obstacles in real time. This study aims to address the challenges and feasibility of deep learning on road signage and obstacles. A model is trained on YOLOv5 using transfer learning method and the performance of the proposed model was evaluated on a test set. The results showed the YOLOv5 achieved 93.5\% mean average precision (mAP). The study concludes that deep learning is a promising method for road signage and road obstacle detection and has potential applications in the field of autonomous vehicles.",

keywords = "Deep learning, Object detection, YOLOv5",

author = "Juen, \{Lee Cheng\} and Khairuddin, \{Ismail Mohd\} and Majeed, \{Anwar P.P.Abdul\} and Abdullah, \{Muhammad Amirul\} and Nasir, \{Ahmad Fakhri Ab\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023 ; Conference date: 22-08-2023 Through 23-08-2023",

year = "2024",

doi = "10.1007/978-981-99-8498-5\_2",

language = "English",

isbn = "9789819984978",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "15--25",

editor = "Andrew Tan and Fan Zhu and Haochuan Jiang and Kazi Mostafa and Yap, \{Eng Hwa\} and Leo Chen and Olule, \{Lillian J. A.\} and Hyun Myung",

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Juen, LC, Khairuddin, IM, Majeed, APPA, Abdullah, MA & Nasir, AFA 2024, Road Signage and Road Obstacle Detection Using Deep Learning Method. in A Tan, F Zhu, H Jiang, K Mostafa, EH Yap, L Chen, LJA Olule & H Myung (eds), Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. Lecture Notes in Networks and Systems, vol. 845, Springer Science and Business Media Deutschland GmbH, pp. 15-25, International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023, Suzhou, China, 22/08/23. https://doi.org/10.1007/978-981-99-8498-5_2

Road Signage and Road Obstacle Detection Using Deep Learning Method. / Juen, Lee Cheng; Khairuddin, Ismail Mohd; Majeed, Anwar P.P.Abdul et al.
Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. ed. / Andrew Tan; Fan Zhu; Haochuan Jiang; Kazi Mostafa; Eng Hwa Yap; Leo Chen; Lillian J. A. Olule; Hyun Myung. Springer Science and Business Media Deutschland GmbH, 2024. p. 15-25 (Lecture Notes in Networks and Systems; Vol. 845).

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

TY - GEN

T1 - Road Signage and Road Obstacle Detection Using Deep Learning Method

AU - Juen, Lee Cheng

AU - Khairuddin, Ismail Mohd

AU - Majeed, Anwar P.P.Abdul

AU - Abdullah, Muhammad Amirul

AU - Nasir, Ahmad Fakhri Ab

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - This study presents a deep learning approach for road signage and road obstacle detection. The purpose of this research was to train a robust and efficient method for detecting road signs and obstacles in real time. This study aims to address the challenges and feasibility of deep learning on road signage and obstacles. A model is trained on YOLOv5 using transfer learning method and the performance of the proposed model was evaluated on a test set. The results showed the YOLOv5 achieved 93.5% mean average precision (mAP). The study concludes that deep learning is a promising method for road signage and road obstacle detection and has potential applications in the field of autonomous vehicles.

AB - This study presents a deep learning approach for road signage and road obstacle detection. The purpose of this research was to train a robust and efficient method for detecting road signs and obstacles in real time. This study aims to address the challenges and feasibility of deep learning on road signage and obstacles. A model is trained on YOLOv5 using transfer learning method and the performance of the proposed model was evaluated on a test set. The results showed the YOLOv5 achieved 93.5% mean average precision (mAP). The study concludes that deep learning is a promising method for road signage and road obstacle detection and has potential applications in the field of autonomous vehicles.

KW - Deep learning

KW - Object detection

KW - YOLOv5

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DO - 10.1007/978-981-99-8498-5_2

M3 - Conference Proceeding

AN - SCOPUS:85187798753

SN - 9789819984978

T3 - Lecture Notes in Networks and Systems

SP - 15

EP - 25

BT - Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023

A2 - Tan, Andrew

A2 - Zhu, Fan

A2 - Jiang, Haochuan

A2 - Mostafa, Kazi

A2 - Yap, Eng Hwa

A2 - Chen, Leo

A2 - Olule, Lillian J. A.

A2 - Myung, Hyun

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

Y2 - 22 August 2023 through 23 August 2023

ER -

Juen LC, Khairuddin IM, Majeed APPA, Abdullah MA, Nasir AFA. Road Signage and Road Obstacle Detection Using Deep Learning Method. In Tan A, Zhu F, Jiang H, Mostafa K, Yap EH, Chen L, Olule LJA, Myung H, editors, Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 15-25. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-99-8498-5_2

Road Signage and Road Obstacle Detection Using Deep Learning Method

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