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Related Concept Videos

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Related Experiment Video

Updated: May 1, 2026

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YOLOv8n-RF: A Dynamic Remote Control Finger Recognition Method for Suppressing False Detection.

Yawen Wang1, Gaofeng Wang1, Yining Yao1

  • 1College of Computer Science and Engineering, Xi'an Technological University, Xi'an 710021, China.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
Summary

A new YOLOv8n-Remote Finger (YOLOv8n-RF) algorithm improves gesture recognition for smart TVs by reducing false detections and costs. This advanced finger detection method enhances user interaction and system efficiency.

Keywords:
YOLOv8n-RFattention mechanismdeep learningmulti-scale featuresremote-controlled finger recognition

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Gesture interaction is an emerging human-computer interaction (HCI) method for smart TVs.
  • Existing gesture recognition algorithms face challenges with false detection and high computational costs.
  • Accurate and efficient gesture recognition is crucial for seamless smart TV operation.

Purpose of the Study:

  • To propose an optimized algorithm for dynamic remote control finger detection in smart TV gesture interaction.
  • To address the limitations of existing methods regarding accuracy, cost, and false detection rates.
  • To enhance the overall user experience and reliability of smart TV gesture controls.

Main Methods:

  • Development of the YOLOv8n-Remote Finger (YOLOv8n-RF) algorithm, a novel approach for finger detection.
  • Integration of the CRVB-DSConvEMA module within the feature extraction network.
  • Implementation of the SPPF-DSConvEMA module in the downsampling process and BiFPN in the Neck layer.
  • Validation using the self-made Remote Finger dataset and the public HaGRID dataset.

Main Results:

  • The YOLOv8n-RF algorithm demonstrated improved mean Average Precision (mAP) by 1.23% on the Remote Finger dataset and 0.84% on the HaGRID dataset compared to YOLOv8n.
  • Significant reductions were observed in model size (2.49 M), GFLOPs (1.7), and the false detection rate (22%).
  • The algorithm achieves low cost and complexity, meeting practical deployment requirements.

Conclusions:

  • The proposed YOLOv8n-RF algorithm offers a superior solution for dynamic remote control finger detection in smart TVs.
  • The enhancements in accuracy and efficiency contribute to reducing false control operations and improving user interaction.
  • This research provides a valuable contribution to the field of HCI, paving the way for more reliable gesture-based interfaces.