To solve the problems such as large number of parameters and high computational complexity of the high-resolution human pose estimation networks， a lightweight Sandglass Coordinate Attention Network （SCANet） based on High-Resolution Network （HRNet） was proposed for human pose estimation. The Sandglass module and the Coordinate Attention （CoordAttention） module were first introduced； then two lightweight modules， the Sandglass Coordinate Attention bottleneck （SCAneck） module and the Sandglass Coordinate Attention basicblock （SCAblock） module， were built on this basis to obtain the long-range dependence and accurate position information of the spatial direction of the feature map while reducing the amount of model parameters and computational complexity. Experimental results show that with the same image resolution and environmental configuration， SCANet model reduces the number of parameters by 52.6% and the computational complexity by 60.6% compared with HRNet model on Common Objects in COntext （COCO） validation set； the number of parameters and computational complexity of SCANet model are reduced by 52.6% and 61.1% respectively compared with those of HRNet model on Max Planck Institute for Informatics （MPII） validation set； compared with common human pose estimation networks such as Stacked Hourglass Network （Hourglass）， Cascaded Pyramid Network （CPN） and SimpleBaseline， SCANet model can still achieve high-precision prediction of key points of the human body with fewer parameters and lower computational complexity.
