  Hand gesture recognition (HGR) is a vital component in enhancing the
human-computer interaction experience, particularly in multimedia applications,
such as virtual reality, gaming, smart home automation systems, etc. Users can
control and navigate through these applications seamlessly by accurately
detecting and recognizing gestures. However, in a real-time scenario, the
performance of the gesture recognition system is sometimes affected due to the
presence of complex background, low-light illumination, occlusion problems,
etc. Another issue is building a fast and robust gesture-controlled
human-computer interface (HCI) in the real-time scenario. The overall objective
of this paper is to develop an efficient hand gesture detection and
classification model using a channel-pruned YOLOv5-small model and utilize the
model to build a gesture-controlled HCI with a quick response time (in ms) and
higher detection speed (in fps). First, the YOLOv5s model is chosen for the
gesture detection task. Next, the model is simplified by using a channel-pruned
algorithm. After that, the pruned model is further fine-tuned to ensure
detection efficiency. We have compared our suggested scheme with other
state-of-the-art works, and it is observed that our model has shown superior
results in terms of mAP (mean average precision), precision (\%), recall (\%),
and F1-score (\%), fast inference time (in ms), and detection speed (in fps).
Our proposed method paves the way for deploying a pruned YOLOv5s model for a
real-time gesture-command-based HCI to control some applications, such as the
VLC media player, Spotify player, etc., using correctly classified gesture
commands in real-time scenarios. The average detection speed of our proposed
system has reached more than 60 frames per second (fps) in real-time, which
meets the perfect requirement in real-time application control.
