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Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor

One of the safety features that can alert drivers to the presence of other vehicles and reduce the risk of collisions is vehicle detection. In this study, the objective is to setup a driving support system for detecting vehicles, motorcycles, and traffic signals on the roads near to Universiti Malay...

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Main Authors: Beg, Mohammad Sojon, Muhammad Yusri, Ismail, Miah, Md Saef Ullah, Mohamad Heerwan, Peeie
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2023
Subjects:
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
Online Access:http://umpir.ump.edu.my/id/eprint/38883/1/Enhancing%20Driving%20Assistance%20System%20with%20YOLO%20V8-Based%20Normal%20Visual%20Camera%20Sensor.pdf
http://umpir.ump.edu.my/id/eprint/38883/
https://doi.org/10.37934/araset.31.1.226236
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spelling my.ump.umpir.388832023-10-16T03:57:24Z http://umpir.ump.edu.my/id/eprint/38883/ Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor Beg, Mohammad Sojon Muhammad Yusri, Ismail Miah, Md Saef Ullah Mohamad Heerwan, Peeie TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery One of the safety features that can alert drivers to the presence of other vehicles and reduce the risk of collisions is vehicle detection. In this study, the objective is to setup a driving support system for detecting vehicles, motorcycles, and traffic signals on the roads near to Universiti Malaysia Pahang using object detection techniques. The video was taken through a direct camera to capture video footage of traffic objects on the roads in the district, which was then analysed using the YOLO-V8 deep learning algorithm. The system was trained on a primary dataset of 1,068 images, with 70% of the dataset used for training, 20% for testing and 10% for validation. After conducting a performance validation, the system achieved a mean average precision (mAP) of 88.2% on train dataset and was able to detect different types of vehicles such as cars, motorcycles, and traffic lights. The results of this study could be beneficial for road safety authorities and researchers interested in developing intelligent transportation systems. Semarak Ilmu Publishing 2023 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/38883/1/Enhancing%20Driving%20Assistance%20System%20with%20YOLO%20V8-Based%20Normal%20Visual%20Camera%20Sensor.pdf Beg, Mohammad Sojon and Muhammad Yusri, Ismail and Miah, Md Saef Ullah and Mohamad Heerwan, Peeie (2023) Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor. Journal of Advanced Research in Applied Sciences and Engineering Technology, 31 (1). pp. 226-236. ISSN 2462-1943. (Published) https://doi.org/10.37934/araset.31.1.226236 10.37934/araset.31.1.226236
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 TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
spellingShingle TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
Beg, Mohammad Sojon
Muhammad Yusri, Ismail
Miah, Md Saef Ullah
Mohamad Heerwan, Peeie
Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor
description One of the safety features that can alert drivers to the presence of other vehicles and reduce the risk of collisions is vehicle detection. In this study, the objective is to setup a driving support system for detecting vehicles, motorcycles, and traffic signals on the roads near to Universiti Malaysia Pahang using object detection techniques. The video was taken through a direct camera to capture video footage of traffic objects on the roads in the district, which was then analysed using the YOLO-V8 deep learning algorithm. The system was trained on a primary dataset of 1,068 images, with 70% of the dataset used for training, 20% for testing and 10% for validation. After conducting a performance validation, the system achieved a mean average precision (mAP) of 88.2% on train dataset and was able to detect different types of vehicles such as cars, motorcycles, and traffic lights. The results of this study could be beneficial for road safety authorities and researchers interested in developing intelligent transportation systems.
format Article
author Beg, Mohammad Sojon
Muhammad Yusri, Ismail
Miah, Md Saef Ullah
Mohamad Heerwan, Peeie
author_facet Beg, Mohammad Sojon
Muhammad Yusri, Ismail
Miah, Md Saef Ullah
Mohamad Heerwan, Peeie
author_sort Beg, Mohammad Sojon
title Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor
title_short Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor
title_full Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor
title_fullStr Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor
title_full_unstemmed Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor
title_sort enhancing driving assistance system with yolo v8-based normal visual camera sensor
publisher Semarak Ilmu Publishing
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/38883/1/Enhancing%20Driving%20Assistance%20System%20with%20YOLO%20V8-Based%20Normal%20Visual%20Camera%20Sensor.pdf
http://umpir.ump.edu.my/id/eprint/38883/
https://doi.org/10.37934/araset.31.1.226236
_version_ 1822923773969432576
score 13.232414

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