关键词: 深度学习, 卷积神经网络（CNN）, 掩膜区域卷积神经网络（Mask R-CNN）, 重组通道网络, 人体关键点检测

Abstract:

To solve the problem that the computational cost of Mask R-CNN is too high and the training time is too long due to the large number of iterations, which caused by the parameter quantity in the deep learning solution of the human keypoint detection problem. An improvement of Mask R-CNN network model is proposed, which based on ShuffleNet network. The reassembly channel network of ShuffleNet is taken by this network model, through using group point-to-point convolution and channel shuffle operations and combining the calculation results of the bounding regression with the mask representation, The results show that the improved network model can speed up the operation and reduce the detection time in solving the problem of human key point detection in multi-person situations with preserving accuracy.

Key words: deep learning, Convolution Neural Network（CNN）, Mask R-CNN, ShuffleNet, human keypoint detection