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Investigation of deep learning model for vehicle classification

The usage of automobiles in cities and metropolitan areas has increased drastically throughout the years and there is a need to monitor the flow of road traffic to improve the traffic congestion and safety. One of the best ways to monitor the traffic is using an artificial intelligence and machine l...

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Main Authors: Ahsiah, Ismail, Amelia Ritahani, Ismail, Adzreen Nulsyazwan, S. Nadzeer, Asmarani Ahmad Puzi, asmarani@iium.edu.my, Suryanti, Awang, Roziana, Ramli
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2026
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/43520/1/Investigation%20of%20deep%20learning%20model%20for%20vehicle%20classification.pdf
http://umpir.ump.edu.my/id/eprint/43520/
https://doi.org/10.37934/araset.62.2.6676
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spelling my.ump.umpir.435202025-01-08T04:22:04Z http://umpir.ump.edu.my/id/eprint/43520/ Investigation of deep learning model for vehicle classification Ahsiah, Ismail Amelia Ritahani, Ismail Adzreen Nulsyazwan, S. Nadzeer Asmarani Ahmad Puzi, asmarani@iium.edu.my Suryanti, Awang Roziana, Ramli QA75 Electronic computers. Computer science The usage of automobiles in cities and metropolitan areas has increased drastically throughout the years and there is a need to monitor the flow of road traffic to improve the traffic congestion and safety. One of the best ways to monitor the traffic is using an artificial intelligence and machine learning. An automatic vehicle tracking system based on artificial intelligence and machine learning can offers capability to analyse the real-time traffic video data for the purpose of traffic surveillance. The computer vision is one of the subsets in machine learning that can train the computer to understand the visual data and perform specific tasks such as object detection and classification. A Vision-based system can be proposed to detect road accidents, predict traffic congestion and further road traffic analytics. This can improve the safety in transportation where it can recognize types of vehicles on the road, detecting road accidents, predicting the traffic congestion and further road traffic analytics. In the context of road traffic monitoring, the parameters of the traffic such as the type and number of vehicles that passes through must be recorded in order to gain valuable insights and make prediction such as the occurrence of traffic congestion. However, this requires reliable informative and accurate data as input for analytics. Therefore, in this research the deep learning model for vehicle classification is investigated to detect, classify types of vehicles and further predictive analytics. The vehicle classification is proposed based on Single Shot Detector (SSD) architecture model. The proposed model is tested on five different classes of vehicles with a total of 1263 images. Experimental results show that SSD model able to achieve 0.721 of precision, 0.741 of recall and 0.731 of F1 Score. Finally, the result show that the SSD model is more accurate among all the models for all the performance measure with the difference of more than 0.052 of precision, 0.706 of recall and 0.05 of F1 Score. Semarak Ilmu Publishing 2026 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/43520/1/Investigation%20of%20deep%20learning%20model%20for%20vehicle%20classification.pdf Ahsiah, Ismail and Amelia Ritahani, Ismail and Adzreen Nulsyazwan, S. Nadzeer and Asmarani Ahmad Puzi, asmarani@iium.edu.my and Suryanti, Awang and Roziana, Ramli (2026) Investigation of deep learning model for vehicle classification. Journal of Advanced Research in Applied Sciences and Engineering Technology, 62 (2). pp. 66-76. ISSN 2462-1943. (Published) https://doi.org/10.37934/araset.62.2.6676 10.37934/araset.62.2.6676
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 QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ahsiah, Ismail
Amelia Ritahani, Ismail
Adzreen Nulsyazwan, S. Nadzeer
Asmarani Ahmad Puzi, asmarani@iium.edu.my
Suryanti, Awang
Roziana, Ramli
Investigation of deep learning model for vehicle classification
description The usage of automobiles in cities and metropolitan areas has increased drastically throughout the years and there is a need to monitor the flow of road traffic to improve the traffic congestion and safety. One of the best ways to monitor the traffic is using an artificial intelligence and machine learning. An automatic vehicle tracking system based on artificial intelligence and machine learning can offers capability to analyse the real-time traffic video data for the purpose of traffic surveillance. The computer vision is one of the subsets in machine learning that can train the computer to understand the visual data and perform specific tasks such as object detection and classification. A Vision-based system can be proposed to detect road accidents, predict traffic congestion and further road traffic analytics. This can improve the safety in transportation where it can recognize types of vehicles on the road, detecting road accidents, predicting the traffic congestion and further road traffic analytics. In the context of road traffic monitoring, the parameters of the traffic such as the type and number of vehicles that passes through must be recorded in order to gain valuable insights and make prediction such as the occurrence of traffic congestion. However, this requires reliable informative and accurate data as input for analytics. Therefore, in this research the deep learning model for vehicle classification is investigated to detect, classify types of vehicles and further predictive analytics. The vehicle classification is proposed based on Single Shot Detector (SSD) architecture model. The proposed model is tested on five different classes of vehicles with a total of 1263 images. Experimental results show that SSD model able to achieve 0.721 of precision, 0.741 of recall and 0.731 of F1 Score. Finally, the result show that the SSD model is more accurate among all the models for all the performance measure with the difference of more than 0.052 of precision, 0.706 of recall and 0.05 of F1 Score.
format Article
author Ahsiah, Ismail
Amelia Ritahani, Ismail
Adzreen Nulsyazwan, S. Nadzeer
Asmarani Ahmad Puzi, asmarani@iium.edu.my
Suryanti, Awang
Roziana, Ramli
author_facet Ahsiah, Ismail
Amelia Ritahani, Ismail
Adzreen Nulsyazwan, S. Nadzeer
Asmarani Ahmad Puzi, asmarani@iium.edu.my
Suryanti, Awang
Roziana, Ramli
author_sort Ahsiah, Ismail
title Investigation of deep learning model for vehicle classification
title_short Investigation of deep learning model for vehicle classification
title_full Investigation of deep learning model for vehicle classification
title_fullStr Investigation of deep learning model for vehicle classification
title_full_unstemmed Investigation of deep learning model for vehicle classification
title_sort investigation of deep learning model for vehicle classification
publisher Semarak Ilmu Publishing
publishDate 2026
url http://umpir.ump.edu.my/id/eprint/43520/1/Investigation%20of%20deep%20learning%20model%20for%20vehicle%20classification.pdf
http://umpir.ump.edu.my/id/eprint/43520/
https://doi.org/10.37934/araset.62.2.6676
_version_ 1822924928645595136
score 13.235362

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