VEHICLE CLASSIFICATION USING NEURAL NETWORKS AND IMAGE PROCESSING
Vehicle classification is getting important especially in security systems, surveillance, transportation congestion reduction, and accident prevention. However, it is difficult to classify the traffic objects due to the poor quality of images from videos. Hence, image processing techniques are appli...
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2022
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Online Access: | http://eprints.utem.edu.my/id/eprint/27036/2/0234007102023385.PDF http://eprints.utem.edu.my/id/eprint/27036/ https://ijeeas.utem.edu.my/ijeeas/article/view/6144 https://ijeeas.utem.edu.my/ijeeas/article/view/6144 |
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my.utem.eprints.270362023-12-14T16:46:48Z http://eprints.utem.edu.my/id/eprint/27036/ VEHICLE CLASSIFICATION USING NEURAL NETWORKS AND IMAGE PROCESSING ONG KANG WEI, ONG KANG WEI LOH SER LEE, LOH SER LEE Vehicle classification is getting important especially in security systems, surveillance, transportation congestion reduction, and accident prevention. However, it is difficult to classify the traffic objects due to the poor quality of images from videos. Hence, image processing techniques are applied to increase the accuracy of the result. The aim of this study is to propose a vehicle classification scheme where YOLO v5 algorithm and Faster R-CNN algorithm are being implemented separately into vehicle classification, followed by comparison of result between these two algorithms. In this study, vehicles are classified into five classes, namely motorcycle, car, van, bus and lorry. The labeled dataset is being split into training set and validation set and then trained under algorithm YOLO v5 and Faster R-CNN separately. Experimental results show that YOLO v5 performs better with the mean average Precision, Precision, and Recall rate up to 0.91, 0.81, and 0.86, respectively Penerbit UTeM Press 2022-10 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27036/2/0234007102023385.PDF ONG KANG WEI, ONG KANG WEI and LOH SER LEE, LOH SER LEE (2022) VEHICLE CLASSIFICATION USING NEURAL NETWORKS AND IMAGE PROCESSING. INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING AND APPLIED SCIENCES, 5 (2). pp. 37-46. ISSN 2600-9633 https://ijeeas.utem.edu.my/ijeeas/article/view/6144 https://ijeeas.utem.edu.my/ijeeas/article/view/6144 |
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Vehicle classification is getting important especially in security systems, surveillance, transportation congestion reduction, and accident prevention. However, it is difficult to classify the traffic objects due to the poor quality of images from videos. Hence, image processing techniques are applied to increase the accuracy of the result. The aim of this study is to propose a vehicle classification scheme where YOLO v5 algorithm and Faster R-CNN algorithm are being implemented separately into vehicle classification, followed by comparison of result between these two algorithms. In this study, vehicles are classified into five classes, namely motorcycle, car, van, bus and lorry. The labeled dataset is being split into training set and validation set and then trained under algorithm YOLO v5 and Faster R-CNN separately. Experimental results show that YOLO v5 performs better with the mean average Precision, Precision, and Recall rate up to 0.91, 0.81, and 0.86, respectively |
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Article |
author |
ONG KANG WEI, ONG KANG WEI LOH SER LEE, LOH SER LEE |
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ONG KANG WEI, ONG KANG WEI LOH SER LEE, LOH SER LEE VEHICLE CLASSIFICATION USING NEURAL NETWORKS AND IMAGE PROCESSING |
author_facet |
ONG KANG WEI, ONG KANG WEI LOH SER LEE, LOH SER LEE |
author_sort |
ONG KANG WEI, ONG KANG WEI |
title |
VEHICLE CLASSIFICATION USING NEURAL NETWORKS AND IMAGE PROCESSING |
title_short |
VEHICLE CLASSIFICATION USING NEURAL NETWORKS AND IMAGE PROCESSING |
title_full |
VEHICLE CLASSIFICATION USING NEURAL NETWORKS AND IMAGE PROCESSING |
title_fullStr |
VEHICLE CLASSIFICATION USING NEURAL NETWORKS AND IMAGE PROCESSING |
title_full_unstemmed |
VEHICLE CLASSIFICATION USING NEURAL NETWORKS AND IMAGE PROCESSING |
title_sort |
vehicle classification using neural networks and image processing |
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Penerbit UTeM Press |
publishDate |
2022 |
url |
http://eprints.utem.edu.my/id/eprint/27036/2/0234007102023385.PDF http://eprints.utem.edu.my/id/eprint/27036/ https://ijeeas.utem.edu.my/ijeeas/article/view/6144 https://ijeeas.utem.edu.my/ijeeas/article/view/6144 |
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