TY - JOUR
AU - Yao, Yining
AB - 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.
TI - YOLOv8n-RF: A Dynamic Remote Control Finger Recognition Method for Suppressing False Detection
JO - Sensors (Basel, Switzerland)
DO - 10.3390/s25092768
DA - 2025-04-27
UR - https://www.deepdyve.com/lp/pubmed-central/yolov8n-rf-a-dynamic-remote-control-finger-recognition-method-for-Ce9iXDEWPA
VL - 25
IS - 9
DP - DeepDyve
ER -