PEA-YOLO: a lightweight network for static gesture recognition combining multiscale and attention mechanisms.

Gesture recognition has been widely used in many human–computer interaction applications, which is one of the most intuitive and natural ways for humans to communicate with computers. However, it remains a challenging problem due to the interference such as variety of backgrounds, hand similar object, and lighting changes. In this article, a lightweight static gesture recognition network, named as PEA-YOLO, was put forward. The network adopts the idea of adaptive spatial feature pyramid and combines the attention mechanism and multi-path feature fusion method to improve the localization and recognition performance of gesture features. First, Efficient Channel Attention module was added after the backbone network to focus the model's attention on the gesture. Second, Feature Pyramid Network was replaced by Path Aggregation Network to localize the gesture better. Finally, Adaptive Spatial Feature Fusion module was added before the Yolo head to further reduce false detections rate in gesture recognition. The experiments conducted on the OUHANDS and NUSII datasets show that PEA-YOLO could achieve favorable performance with only 8.57 M parameters in static gesture recognition. Compared with other state of the arts, the proposed lightweight network has obtained a highest accuracy with a much high speed and few parameters. [ABSTRACT FROM AUTHOR]