Summary of Object Detection Using Oriented Window Learning Vi-sion Transformer: Roadway Assets Recognition, by Taqwa Alhadidi et al.
Object Detection using Oriented Window Learning Vi-sion Transformer: Roadway Assets Recognition
by Taqwa Alhadidi, Ahmed Jaber, Shadi Jaradat, Huthaifa I Ashqar, Mohammed Elhenawy
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to object detection in transportation systems, specifically for autonomous driving, traffic monitoring, and infrastructure maintenance. The Oriented Window Learning Vision Transformer (OWL-ViT) is a window-oriented method that adapts to object geometry and existence, making it suitable for detecting diverse roadway assets. The study leverages OWL-ViT within a one-shot learning framework to recognize transportation infrastructure components, such as traffic signs, poles, pavement, and cracks. 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to find objects on roads using a special computer model called OWL-ViT. This helps with things like self-driving cars and checking road conditions. The old ways of doing this didn’t work well because there wasn’t much data or the objects looked different from each other. OWL-ViT makes it better by looking at how objects are shaped and where they are on the road. The study tested this model to see if it works well in different situations, and the results show that it does a great job! |
Keywords
» Artificial intelligence » Object detection » One shot » Vision transformer » Vit
