Title: Vehicle recognition using convolution neural network

Authors: Maleika Heenaye-Mamode Khan; Chonnoo Abubakar Siddick Khan; Rengony Mohammad Oumeir

Addresses: Department of Software and Information Systems, University of Mauritius, Mauritius ' Department of Software and Information Systems, University of Mauritius, Mauritius ' Department of Software and Information Systems, University of Mauritius, Mauritius

Abstract: A significant challenge in the development of automatic vehicle make and model recognition (VMMR) is the distinguishing features between the different shapes based on the appearance of objects. The automatic recognition of vehicles based on their geometric shapes is in high demand. The diversity of make and model of vehicles further complicates this process. There are few applications that can recognise vehicles based on their geometric shape. To bridge this gap, convolution neural network (CNN) was adopted to predict the make and model of a car from either the rear view or front view of the vehicle using the pre-trained MobileNet. First, YOLOV3 has been used to detect the vehicle. The colour and the license plate of the vehicles are also extracted. An accuracy of 94.1% in the recognition of make of cars, 98.7% for the model, 99.1% for car plate registration number, and 90.3% for the colour was achieved.

Keywords: convolution neural network; CNN; deep learning; segmentation; vehicle make and model recognition; VMMR.

DOI: 10.1504/IJBM.2023.130638

International Journal of Biometrics, 2023 Vol.15 No.3/4, pp.344 - 358

Received: 11 Aug 2021
Accepted: 02 Dec 2021
Published online: 02 May 2023 *

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