  Object detection is a critical component of transportation systems,
particularly for applications such as autonomous driving, traffic monitoring,
and infrastructure maintenance. Traditional object detection methods often
struggle with limited data and variability in object appearance. The Oriented
Window Learning Vision Transformer (OWL-ViT) offers a novel approach by
adapting window orientations to the geometry and existence of objects, making
it highly suitable for detecting diverse roadway assets. This study leverages
OWL-ViT within a one-shot learning framework to recognize transportation
infrastructure components, such as traffic signs, poles, pavement, and cracks.
This study presents a novel method for roadway asset detection using OWL-ViT.
We conducted a series of experiments to evaluate the performance of the model
in terms of detection consistency, semantic flexibility, visual context
adaptability, resolution robustness, and impact of non-max suppression. The
results demonstrate the high efficiency and reliability of the OWL-ViT across
various scenarios, underscoring its potential to enhance the safety and
efficiency of intelligent transportation systems.
