IRVD: A Large-Scale Dataset for Classification of Iranian Vehicles in Urban Streets

In recent years, vehicle classification has been one of the most important research topics. However, due to the lack of a proper dataset, this field has not been well-developed as other fields of intelligent traffic management. Therefore, the preparation of large-scale datasets of vehicles for each country is of great interest. In this paper, we introduce a new standard dataset of popular Iranian vehicles. This dataset, which consists of the images of the vehicles moving in urban streets and highways, can be used for vehicle classification and license plate recognition. It contains a large collection of vehicle images in different weather and lighting conditions with different viewing angles. It took more than a year to construct this dataset. The images were taken from various types of mounted cameras with different resolutions and at different altitudes. In order to estimate the complexity of the dataset, some classical methods alongside the popular deep neural networks were trained and evaluated on the dataset. Furthermore, two light-weight CNN structures are also proposed, one with three Conv layers and the other with five Conv layers. The 5-Conv model with 152K parameters reached the recognition rate of 99.09% and could process 48 frames per second on CPU, which is suitable for real-time applications.