VEHICLE CLASSIFICATION USING NEURAL NETWORKS AND IMAGE PROCESSING

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

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Abstract

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

Item Type:	Article
Uncontrolled Keywords:	Faster R-CNN algorithm, Neural network training, Vehicle classification, YOLO v5 algorithm
Divisions:	Faculty of Electrical Engineering
Depositing User:	WIZANA ABD JALIL
Date Deposited:	14 Dec 2023 16:46
Last Modified:	14 Dec 2023 16:46
URI:	http://eprints.utem.edu.my/id/eprint/27036
Statistic Details:	View Download Statistic

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