Abstract
In the past few years, traditional automatic license plate recognition (ALPR) has received great attention to detect unauthorized vehicles by validating their registration numbers. However, fake license plates have made this task more challenging. This paper introduces a multi-level system to deal with the problem of fake license plates using computer vision and deep learning techniques to extract several other important features such as vehicle color, make and model along with the license plate recognition, which can be beneficial in detecting the illegal vehicles. It can be used by security and law enforcement agencies since stolen vehicles or those used in terrorist and robbery activities normally have a fake license plate attached to them. YOLOv3 is applied for vehicle type detection (i.e. car, motorcycle, bus, truck) while an efficient approach for the license plate text recognition has also been implemented. Make and model recognition of a vehicle using ResNet-152 and Xception are the novel contribution in this paper as these deep learning architectures have never been investigated in this context. Different convolutional neural networks (CNNs) are trained and tested using the Stanford Cars-196 dataset where Xception outperformed previous approaches with 96.7% accuracy. A novel deep neural network for vehicle color recognition has also been introduced in this paper, which is not only computationally inexpensive but also outperforms other competitive methods on the vehicle color dataset.
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This research work was funded by the Higher Education Commission (HEC) Pakistan and Ministry of Planning Development and Reforms under the National Center in Big Data and Cloud Computing.
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Hassan, A., Ali, M., Durrani, M.N., Tahir, M.A. (2022). Vehicle Recognition Using Multi-level Deep Learning Models. In: Jawawi, D.N.A., Bajwa, I.S., Kazmi, R. (eds) Engineering Software for Modern Challenges. ESMoC 2021. Communications in Computer and Information Science, vol 1615. Springer, Cham. https://doi.org/10.1007/978-3-031-19968-4_11
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