When it comes to the administration of highways, the identification of intelligent vehicles is becoming an increasingly important component. Applications for vehicle detection are essential for both military and civilian usage, including the monitoring of highway traffic, administration, and the control of urban traffic. The vast amount of vehicle image data available online has the potential to encourage the development of increasingly sophisticated object recognition and classification models and algorithms. But finding an organized, balanced, and meaningful dataset continues to be a major challenge. This study proposes Sorokh-Poth, a new complete balanced image dataset based on Bangladeshi road travel that is compatible with a number of CNN-based architectures. The majority of the photographs in the collection were taken with a smartphone. The dataset includes 9,809 classified and annotated images of ten different types of vehicles, including autorickshaws, bicycles, buses, cars, CNG-powered vehicles, lagoon rickshaws, trucks, and vans. This research work utilizes the residual network ResNet-50 model, a CNN-based architecture. Here, features specific to the type of vehicle were automatically retrieved and grouped. Accuracy, precision, recall, and f1-score were just a few of the metrics used for evaluation during the research. The proposed model exhibited an increasing accuracy despite the vehicles shifting physical characteristics. The purpose of this work is to implement various CNN-based residual networks ResNet-50 and ResNet-150V2. Our proposed ResNet-50 model achieves an accuracy of 98.00% in the detection of native Bangladeshi vehicles, according to result comparisons and evaluations.

Sorokh-Poth

ResNet-50

deep learning

ResNet-150V2

transfer learning

Bangladeshi vehicle classification