Car recognition systems have been gaining popularity in recent years due to their ability to identify the make, model, and year of cars from images. In this paper, we present the building of accurate car recognition systems by using neural networks. This paper proposes a car recognition system using the convolution neural network and VMMRdb dataset and the ResNet-152 model, which provides state-of-the-art performance in image classification tasks. The performance of the proposed model is compared with the GG16 model. The experimental results show that the developed car recognition system achieved high accuracy of 54.4% and efficiency in identifying the cars which has important applications in traffic analysis, theft prevention, and marketing research. We also compared the performance of our model with that of other state-of-the-art car recognition systems, including the popular VGG16 model. This object recognition system has practical applications in various fields, such as law enforcement, parking access control, and traffic monitoring, and can help identify stolen or wanted vehicles quickly and efficiently.
Car Recognition System Using Convolutional Neural Network
14.09.2023
698907 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Traffic sign recognition using weighted multi‐convolutional neural network
Wiley | 2018
|Traffic sign recognition using weighted multi-convolutional neural network
IET | 2018
|