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Real Time American Sign Language Detection Using Yolo-v9

arXiv 2407.17950 ↗First posted 25 Jul 2024cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

This paper focuses on real-time American Sign Language Detection. YOLO is a convolutional neural network (CNN) based model, which was first released in 2015. In recent years, it gained popularity for its real-time detection capabilities. Our study specifically targets YOLO-v9 model, released in 2024. As the model is newly introduced, not much work has been done on it, especially not in Sign Language Detection. Our paper provides deep insight on how YOLO- v9 works and better than previous model.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 25 Jul 2024.

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References · 15

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  2. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  3. 2015Fast R-CNNcited by 2,930
  4. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  5. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  6. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  7. 2018Path Aggregation Network for Instance Segmentationcited by 796
  8. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 508
  9. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 422
  10. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  11. 2021Bottleneck Transformers for Visual Recognitioncited by 364
  12. 2023A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAScited by 149
3 more
  1. 2023Information Bottleneck Analysis of Deep Neural Networks via Lossy Compressioncited by 19
  2. 2023DynamicDet: A Unified Dynamic Architecture for Object Detectioncited by 15
  3. 2024End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Trainingcited by 8

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