Abstract
Vehicle classification is an essential part of intelligent transportation systems (ITS). This work proposes a model based on transfer learning, combining data augmentation for the recognition and classification of local vehicle classes in Canada. It takes inspiration from contemporary deep learning (DL) achievements for image classification. This makes use of the Dataset named Stanford AI of Vehicles, which has 16185 images. The images in this section are divided into 196 types of vehicles. To increase performance further, additional classification blocks are added to the residual network (ResNet-50)-based model which is being used. In this case, vehicle type details are automatically extracted and classified. A number of measures like accuracy, precision, recall, etc. are used during the analysis to evaluate the results. The proposed model exhibits increased accuracy despite vehicles’ different physical characteristics. In comparison to the current baseline method and the two pre-trained DL systems, AlexNet and VGG-16, our suggested method outperforms them all. The suggested ResNet-50 pre-trained model achieves an accuracy of 90.07% in the classification of native vehicle types, according to the outcome comparisons. We also compare this by running VGG-16 where we are getting an accuracy of 82.5%. Along with this vehicle classification, we have implemented number plate detection and smart vehicle counter systems which all together makes our transport system better than ever before.
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Muhib, R.B., Ahmad, I.S., Boufama, B. (2023). Deep Learning-Based Vehicle Classification. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2023, Volume 2. FTC 2023. Lecture Notes in Networks and Systems, vol 814. Springer, Cham. https://doi.org/10.1007/978-3-031-47451-4_18
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