Summary of Real Time American Sign Language Detection Using Yolo-v9, by Amna Imran et al.
Real Time American Sign Language Detection Using Yolo-v9
by Amna Imran, Meghana Shashishekhara Hulikal, Hamza A. A. Gardi
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 research paper investigates real-time American Sign Language detection using YOLO-v9, a convolutional neural network-based model released in 2024. Building upon the popularity of YOLO for its real-time detection capabilities since its inception in 2015, this study provides an in-depth analysis of how YOLO-v9 outperforms previous models in Sign Language Detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Real-time American Sign Language detection is a new area that uses a special model called YOLO. This model was first made public in 2015 and has been very popular ever since because it can detect things quickly. A newer version of this model, YOLO-v9, was released in 2024, but not much work has been done on it specifically for detecting sign language. This study looks at how the new YOLO-v9 model works and compares it to older models. |
Keywords
» Artificial intelligence » Neural network » Yolo
