Vehicle Type, Color and Speed Detection Implementation by Integrating VGG Neural Network and YOLO algorithm utilizing Raspberry Pi Hardware
نویسندگان
1 Faculty of Electrical Engineering, Islamic Azad University Majlisi Branch, Isfahan, Iran.
2 Faculty of Electrical Engineering, Islamic Azad University Majlisi Branch, Isfahan, Iran.
3 Faculty of Electrical Engineering, Islamic Azad University Majlisi Branch, Isfahan, Iran.
doi
10.22044/jadm.2022.11915.2338چکیده
Vehicle type recognition has been widely used in practical applications such as traffic control, unmanned vehicle control, road taxation, smuggling detection, and so on. In this paper, various techniques such as data augmentation and space filtering have been used to improve and enhance the data. Then, a developed algorithm that integrates VGG neural network and YOLO algorithm has been used to detect and identify vehicles, Then the implementation on the Raspberry hardware board and practically through a scenario is mentioned. Real including image data sets are analyzed. The results show the good performance of the implemented algorithm in terms of detection performance (98%), processing speed, and environmental conditions, which indicates its capability in practical applications with low cost.
