mlsanityData to 7 Oct 2026

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

arXiv 2406.10712 ↗First posted 15 Jun 2024cs.CV cs.AIcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 14 references that are in the map. First posted on arXiv 15 Jun 2024.

Builds on

10 of 14 references.

References · 10

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2020End-to-End Object Detection with Transformerscited by 7,384
  4. 2021Transformers in Vision: A Surveycited by 1,009
  5. 2018Zero-Shot Object Detectioncited by 189
  6. 2020OS2D: One-Stage One-Shot Object Detection by Matching Anchor Featurescited by 35
  7. 2023Open-Vocabulary Object Detection using Pseudo Caption Labelscited by 13
  8. 2020Any-Shot Object Detectioncited by 10
  9. 2021A Survey of Deep Learning for Low-Shot Object Detectioncited by 6
  10. 2020A Broad Dataset is All You Need for One-Shot Object Detectioncited by 1

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Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2024Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challengescited by 21
  2. 2024Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testingcited by 10
  3. 2024Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarioscited by 6
  4. 2025RAU: Reference-based Anatomical Understanding with Vision Language Modelscited by 3
  5. 2025Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networkscited by 0
  6. 2025Zero-Shot Scene Understanding with Multimodal Large Language Models for Automated Vehiclescited by 0