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Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm

The issue of road accidents endangering human life has become a global concern due to the rise in traffic volumes. This article presents the evaluation of an object detection model for University of Malaysia Pahang (UMP) roadside conditions, focusing on the detection of vehicles, motorcycles, and tr...

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Main Authors: Beg, Mohammad Sojon, Muhammad Yusri, Ismail, Miah, M. Saef Ullah
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2024
Subjects:
TJ Mechanical engineering and machinery
TL Motor vehicles. Aeronautics. Astronautics
Online Access:http://umpir.ump.edu.my/id/eprint/40027/1/Evaluating%20the%20Performance%20of%20a%20Visual%20Support%20System%20for%20Driving%20Assistance%20using%20a%20Deep%20Learning%20Algorithm.pdf
http://umpir.ump.edu.my/id/eprint/40027/
https://doi.org/10.37934/araset.34.1.3850
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spelling my.ump.umpir.400272024-01-16T04:59:20Z http://umpir.ump.edu.my/id/eprint/40027/ Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm Beg, Mohammad Sojon Muhammad Yusri, Ismail Miah, M. Saef Ullah TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics The issue of road accidents endangering human life has become a global concern due to the rise in traffic volumes. This article presents the evaluation of an object detection model for University of Malaysia Pahang (UMP) roadside conditions, focusing on the detection of vehicles, motorcycles, and traffic lamps. The dataset consists of the driving distance from Hospital Pekan to the University of Malaysia Pahang. Around one thousand images were selected in Roboflow for the train dataset. The model utilises the YOLO V8 deep learning algorithm in the Google Colab environment and is trained using a custom dataset managed by the Roboflow dataset manager. The dataset comprises a diverse set of training and validation images, capturing the unique characteristics of Malaysian roads. The train model's performance was assessed using the F1 score, precision, and recall, with results of 71%, 88.2%, and 84%, respectively. A comprehensive comparison with validation results has shown the efficacy of the proposed model in accurately detecting vehicles, motorcycles, and traffic lamps in real-world Malaysian road scenarios. This study contributes to the improvement of intelligent transportation systems and road safety in Malaysia. Semarak Ilmu Publishing 2024-03 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/40027/1/Evaluating%20the%20Performance%20of%20a%20Visual%20Support%20System%20for%20Driving%20Assistance%20using%20a%20Deep%20Learning%20Algorithm.pdf Beg, Mohammad Sojon and Muhammad Yusri, Ismail and Miah, M. Saef Ullah (2024) Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm. Journal of Advanced Research in Applied Sciences and Engineering Technology, 34 (1). pp. 38-50. ISSN 2462-1943. (Published) https://doi.org/10.37934/araset.34.1.3850 10.37934/araset.34.1.3850
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TJ Mechanical engineering and machinery
TL Motor vehicles. Aeronautics. Astronautics
spellingShingle TJ Mechanical engineering and machinery
TL Motor vehicles. Aeronautics. Astronautics
Beg, Mohammad Sojon
Muhammad Yusri, Ismail
Miah, M. Saef Ullah
Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm
description The issue of road accidents endangering human life has become a global concern due to the rise in traffic volumes. This article presents the evaluation of an object detection model for University of Malaysia Pahang (UMP) roadside conditions, focusing on the detection of vehicles, motorcycles, and traffic lamps. The dataset consists of the driving distance from Hospital Pekan to the University of Malaysia Pahang. Around one thousand images were selected in Roboflow for the train dataset. The model utilises the YOLO V8 deep learning algorithm in the Google Colab environment and is trained using a custom dataset managed by the Roboflow dataset manager. The dataset comprises a diverse set of training and validation images, capturing the unique characteristics of Malaysian roads. The train model's performance was assessed using the F1 score, precision, and recall, with results of 71%, 88.2%, and 84%, respectively. A comprehensive comparison with validation results has shown the efficacy of the proposed model in accurately detecting vehicles, motorcycles, and traffic lamps in real-world Malaysian road scenarios. This study contributes to the improvement of intelligent transportation systems and road safety in Malaysia.
format Article
author Beg, Mohammad Sojon
Muhammad Yusri, Ismail
Miah, M. Saef Ullah
author_facet Beg, Mohammad Sojon
Muhammad Yusri, Ismail
Miah, M. Saef Ullah
author_sort Beg, Mohammad Sojon
title Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm
title_short Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm
title_full Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm
title_fullStr Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm
title_full_unstemmed Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm
title_sort evaluating the performance of a visual support system for driving assistance using a deep learning algorithm
publisher Semarak Ilmu Publishing
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/40027/1/Evaluating%20the%20Performance%20of%20a%20Visual%20Support%20System%20for%20Driving%20Assistance%20using%20a%20Deep%20Learning%20Algorithm.pdf
http://umpir.ump.edu.my/id/eprint/40027/
https://doi.org/10.37934/araset.34.1.3850
_version_ 1822924083498582016
score 13.232414

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