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

## TL;DR

This paper introduces YOLOv8n-RF, a new algorithm for detecting remote control finger gestures in smart TVs, which reduces false detections and improves performance.

## Contribution

The novel YOLOv8n-RF algorithm introduces specific modules to enhance detection accuracy and reduce false positives in dynamic remote control finger recognition.

## Key findings

- The YOLOv8n-RF algorithm improved mean Average Precision (mAP) by 1.23% on a custom dataset and 0.84% on the HaGRID dataset.
- The model size was reduced by 2.49 M, GFLOPs decreased by 1.7, and false detection rate dropped by 22%.
- The algorithm meets low cost and low complexity requirements for smart TV gesture interaction.

## Abstract

Gesture interaction is one of the novel human-computer interaction methods for smart TVs. Addressing the issues of false detection and high detection costs in gesture recognition algorithms for gesture interaction, this paper proposes the YOLOv8n-Remote Finger (YOLOv8n-RF) algorithm for dynamic remote control finger detection. This algorithm utilizes the CRVB-DSConvEMA module in the feature extraction network, adopts the SPPF-DSConvEMA module in the downsampling process, and introduces BiFPN in the Neck layer. Experiments conducted on the self-made Remote Finger dataset and the public HaGRID dataset demonstrated that, compared to the YOLOv8n algorithm, the proposed YOLOv8n-RF algorithm achieved an improvement in mean Average Precision (mAP) by 1.23% and 0.84%, respectively. Additionally, the model size was reduced by 2.49 M, the GFLOPs were decreased by 1.7, and the false detection rate was lowered by 22%. The YOLOv8n-RF algorithm meets the requirements of low cost and low complexity, which contributes to reducing false control operations on smart TVs.