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Vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm

Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a chal...

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Main Authors: Khalifa, Othman Omran, Wajdi, Muhammad H., Saeed, Rashid A., Hassan Abdalla Hashim, Aisha, Ahmed, Muhammed Z., Ali, Elmustafa Sayed
Format: Article
Language:English
Published: Hindawi 2022
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/97245/7/97245_Vehicle%20detection%20for%20vision-based%20intelligent%20transportation%20systems.pdf
http://irep.iium.edu.my/97245/
https://downloads.hindawi.com/journals/jat/2022/9189600.pdf
https://doi.org/10.1155/2022/9189600
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spelling my.iium.irep.972452022-03-18T01:40:35Z http://irep.iium.edu.my/97245/ Vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm Khalifa, Othman Omran Wajdi, Muhammad H. Saeed, Rashid A. Hassan Abdalla Hashim, Aisha Ahmed, Muhammed Z. Ali, Elmustafa Sayed T Technology (General) Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a challenging problem that must be solved. Hardware-based solutions such as radars and LIDAR are been proposed but are too expensive to be maintained and produce little valuable information to human operators at traffic monitoring systems. Software based solutions using traditional algorithms such as Histogram of Gradients (HOG) and Gaussian Mixed Model (GMM) are computationally slow and not suitable for real-time traffic detection. )erefore, the paper will review and evaluate different vehicle detection methods. In addition, a method of utilizing Convolutional Neural Network (CNN) is used for the detection of vehicles from roadway camera outputs to apply video processing techniques and extract the desired information. Specifically, the paper utilized the YOLOv5s architecture coupled with k-means algorithm to perform anchor box optimization under different illumination levels. Results from the simulated and evaluated algorithm showed that the proposed model was able to achieve a mAP of 97.8 in the daytime dataset and 95.1 in the nighttime dataset. Hindawi 2022-03-15 Article PeerReviewed application/pdf en http://irep.iium.edu.my/97245/7/97245_Vehicle%20detection%20for%20vision-based%20intelligent%20transportation%20systems.pdf Khalifa, Othman Omran and Wajdi, Muhammad H. and Saeed, Rashid A. and Hassan Abdalla Hashim, Aisha and Ahmed, Muhammed Z. and Ali, Elmustafa Sayed (2022) Vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm. Journal of Advanced Transportation. pp. 1-11. ISSN 0197-6729 E-ISSN 2042-3195 https://downloads.hindawi.com/journals/jat/2022/9189600.pdf https://doi.org/10.1155/2022/9189600
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Khalifa, Othman Omran
Wajdi, Muhammad H.
Saeed, Rashid A.
Hassan Abdalla Hashim, Aisha
Ahmed, Muhammed Z.
Ali, Elmustafa Sayed
Vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm
description Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a challenging problem that must be solved. Hardware-based solutions such as radars and LIDAR are been proposed but are too expensive to be maintained and produce little valuable information to human operators at traffic monitoring systems. Software based solutions using traditional algorithms such as Histogram of Gradients (HOG) and Gaussian Mixed Model (GMM) are computationally slow and not suitable for real-time traffic detection. )erefore, the paper will review and evaluate different vehicle detection methods. In addition, a method of utilizing Convolutional Neural Network (CNN) is used for the detection of vehicles from roadway camera outputs to apply video processing techniques and extract the desired information. Specifically, the paper utilized the YOLOv5s architecture coupled with k-means algorithm to perform anchor box optimization under different illumination levels. Results from the simulated and evaluated algorithm showed that the proposed model was able to achieve a mAP of 97.8 in the daytime dataset and 95.1 in the nighttime dataset.
format Article
author Khalifa, Othman Omran
Wajdi, Muhammad H.
Saeed, Rashid A.
Hassan Abdalla Hashim, Aisha
Ahmed, Muhammed Z.
Ali, Elmustafa Sayed
author_facet Khalifa, Othman Omran
Wajdi, Muhammad H.
Saeed, Rashid A.
Hassan Abdalla Hashim, Aisha
Ahmed, Muhammed Z.
Ali, Elmustafa Sayed
author_sort Khalifa, Othman Omran
title Vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm
title_short Vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm
title_full Vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm
title_fullStr Vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm
title_full_unstemmed Vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm
title_sort vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm
publisher Hindawi
publishDate 2022
url http://irep.iium.edu.my/97245/7/97245_Vehicle%20detection%20for%20vision-based%20intelligent%20transportation%20systems.pdf
http://irep.iium.edu.my/97245/
https://downloads.hindawi.com/journals/jat/2022/9189600.pdf
https://doi.org/10.1155/2022/9189600
_version_ 1728051172529405952
score 13.211869

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