Summary of Object Detection For Vehicle Dashcams Using Transformers, by Osama Mustafa et al.
Object Detection for Vehicle Dashcams using Transformers
by Osama Mustafa, Khizer Ali, Anam Bibi, Imran Siddiqi, Momina Moetesum
First submitted to arxiv on: 28 Aug 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 proposes a novel approach for object detection in dashcams using transformers, building upon the state-of-the-art DEtection TRansformer (DETR) architecture. By leveraging contextual information, the DETR model demonstrates strong performance in various conditions, including different weather and illumination scenarios. The authors train their model on a dataset representing real-world conditions and achieve an mAP of 0.95 on detection. This intelligent automation can significantly enhance the capabilities of dashcam systems, assisting drivers and fleet management companies to increase their productivity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to improve dashboard cameras. These cameras help drivers and companies keep track of what’s happening around them. The researchers used a special type of AI called transformers to make the camera system better. They tested their system on real-world data and it worked really well, being able to detect objects with 95% accuracy. This technology can help people by making driving safer and more efficient. |
Keywords
» Artificial intelligence » Object detection » Transformer
