Vehicle Type, Color and Speed Detection Implementation by Integrating VGG Neural Network and YOLO algorithm utilizing Raspberry Pi Hardware

Vehicle type recognition has been widely used in practical applications such as traffic control, unmanned vehicle control, road taxation, and smuggling detection. In this work, various techniques such as data augmentation and space filtering are used to improve and enhance the data. Then a developed algorithm that integrates VGG neural network and the YOLO algorithm are used to detect and identify the vehicles. Then the implementation on the Raspberry hardware board and practically through a scenario is mentioned. The real including image datasets are analyzed. The results obtained show the good performance of the implemented algorithm is in terms of detection performance (98%), processing speed, and environmental conditions, which indicates its capability in practical applications with low cost.