Vehicle Detection system

Abstract

Manual vehicle entry at different checkpoints in Bhutan by police personnel creates traffic congestion and delay. Drivers wait in a queue to register vehicles by providing details such as vehicle type and number. There is no automatic system to detect vehicles. The purpose of the study was to create a machine-learning model to detect different types of vehicles in Bhutan. For this study, a total of 20 popular light vehicle classes were identified. The images and videos were captured. The number of frames was extracted from the videos and different types of data augmentation approaches were then adopted to create variations in the curated dataset for greater model generalization. Then different algorithms were evaluated on this dataset. However, the Convolutional Neural Network outperformed all other algorithms. The training and testing accuracy obtained was 99.85% and 99.62% respectively. The model was then deployed using the Flask web framework.