Gesture Object Detection and Recognition Based on YOLOv11
Jian Xu, Heyao Chen, Xingpeng Xiao, Mengyuan Zhao, Bo Liu
Abstract
This article explores the application of YOLOv11 algorithm in gesture recognition field to evaluate its performance in human-computer interaction (HCI). By introducing the YOLOv8 model and corresponding dataset for training, we obtained the confusion matrix predicted by the model, which shows that the model can accurately recognize most gestures, although there are a few cases of misidentification. When the IoU threshold is 0.5, the average accuracy (mAP) of the model steadily improves with the progress of training, indicating that the overall performance of the model in gesture detection tasks has been enhanced. In addition, even under stricter evaluation conditions where the IoU threshold was increased from 0.5 to 0.95, mAP still showed an upward trend, although the growth rate was not as significant as mAP50, which still demonstrated the improvement in model performance. Through the detection of the test set images, we found that the YOLOv11 model can effectively recognize gestures and accurately interpret their meanings, demonstrating high accuracy. This study not only demonstrates the potential of YOLOv11 in gesture recognition tasks, but also provides a new technological path for the future development of HCI field. Overall, the YOLOv11 algorithm has demonstrated strong performance and accuracy in gesture recognition, providing a more natural and intuitive way for interaction between smart devices and humans.