关键词: 嵌入式神经网络加速, 手势检测, 卷积神经网络, SSD

Abstract: Aiming at solving the problems that the convolutional neural network runs slowly and people can not detect fastly gesture under embedded platform, this paper proposes an embedded gesture detection algorithm based on convolution neural network, which significantly improves the speed of gesture detection and maintains high accuracy. Firstly, a new preprocessing method is used to extend the original gesture database by 5 times. Then, the original neural network layer of the Single Shot MultiBox Detector（SSD） algorithm is changed, and a 3 times acceleration under the CPU is obtained by using the MobileNet neural network. Finally, using different sizes of input pictures and changing the network structure at the same time, the amount of calculation of the algorithm is greatly reduced. The experimental results show that the proposed algorithm reduces mean Average Precision（mAP） by 2.7% in two datasets, but under the Qualcomm SnapDragon 820 platform, a picture can be detected to reach 0.233 s and the detection speed is increased by more than 40 times.

Key words: embedded neural network acceleration, gesture detection, convolutional neural network, Single Shot MultiBox Detector（SSD）