Real Time Bangladeshi Vehicle type Recognition Vsing Yolov9 Variants

Abstract

Vehicle type recognition is important for supporting traffic management and urban planning, as Bangladesh sees rapid growth of vehicular traffic. In this study, I provide a dataset and methodology for building a Bangladeshi vehicle type recognition model using YOLOv9 variant. The dataset gathered from traffic signal points in Bangladesh contains 281 images belonging to 12 vehicle classes which has been augmented to 402 images by techniques such as image augmentation (eg; horizontal flipping, adjusting brightness, etc). I applied preprocessing steps like auto orientation, resize to 256x256 and histogram equalization to improve data quality. I trained Google Colab YoloV9-C, YoloV9-E, and YoloV9-Gelan C with batch size of 32, for 100 epochs, then evaluated them based on mean average precision (mAP). Among all the models, YoloV9-E could achieve the best mAP of 73.66%, which indicates that it was able to perform well in real-time vehicle detection. Based on these insights, the trained models were deployed on Streamlit for testing in real-world Bangladeshi traffic environments.