Vehicles Classification and Brand Recognition Using Convolution Network and Neural Networks

Autonomous vehicles are in full development and vehicles classification is a fundamental part of this new technology. To interact with other objects on the road, vehicles need to be able to identify what is surrounding them. In this paper, we develop a platform named VARC (Vehicle Algorithm for Recognition and Classification) integrating a fully connected neural network to classify different types of vehicles such as trucks. Moreover, VARC considers the detected type to identify the brand of the vehicle using a convolutional neural network. This allows getting valuable characteristics of the vehicle like its color, the number of passengers. By processing pictures taken from security cameras or from ones on vehicles, VARC may help cops identify stolen cars and autonomous vehicles improve their perception of their environment. VARC's neural network is trained on more than 7000 images of cars, trucks, and motorcycles. Results demonstrated the effectiveness of VARC in terms of generating valuable data while minimizing the needed resources.