# Object Detection using Oriented Window Learning Vi-sion Transformer: Roadway Assets Recognition

arXiv: https://arxiv.org/abs/2406.10712 · HTML: https://mlsanity.com/paper/2406.10712/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 15 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Zero-Shot Object Detection](https://mlsanity.com/paper/1804.04340/) (2018)
- [OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features](https://mlsanity.com/paper/2003.06800/) (2020)
- [Open-Vocabulary Object Detection using Pseudo Caption Labels](https://mlsanity.com/paper/2303.13040/) (2023)
- [Any-Shot Object Detection](https://mlsanity.com/paper/2003.07003/) (2020)
- [A Survey of Deep Learning for Low-Shot Object Detection](https://mlsanity.com/paper/2112.02814/) (2021)
- [A Broad Dataset is All You Need for One-Shot Object Detection](https://mlsanity.com/paper/2011.04267/) (2020)

## Built on by (most cited first, 6 of 6)

- [Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges](https://mlsanity.com/paper/2407.00092/) (2024)
- [Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing](https://mlsanity.com/paper/2409.18286/) (2024)
- [Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios](https://mlsanity.com/paper/2408.00948/) (2024)
- [RAU: Reference-based Anatomical Understanding with Vision Language Models](https://mlsanity.com/paper/2509.22404/) (2025)
- [Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks](https://mlsanity.com/paper/2503.21956/) (2025)
- [Zero-Shot Scene Understanding with Multimodal Large Language Models for Automated Vehicles](https://mlsanity.com/paper/2506.12232/) (2025)

## Abstract (arXiv)

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.