Abstract
To solve the problem that the lightweight object detection network has insufficient ability to extract static gesture features, high false detection rate and missed detection rate, a lightweight gesture recognition algorithm is proposed based on YOLOv4-tiny network structure. First, a more powerful and low-cost ghost feature mapping is introduced to enhance the ability of network to obtain multi-scale gesture features. Then, the embedded channel attention mechanism realizes feature recalibration and achieves the purpose of reducing background interference. Finally, Swish is used as the main activation function to further improve the accuracy of gesture recognition. The experimental results on the gesture dataset show that the proposed algorithm has better recognition performance than YOLOv4-tiny. For multi-scale gestures under different environmental conditions, the algorithm achieves accurate classification as well as real-time detection, and has better recognition performance for small-scale gestures.
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