FastNet: Fast high-resolution network for human pose estimation

•FastNet outperforms popular lightweight networks on human pose estimation.•The asymmetric bottleneck module can extract keypoint features with multi-scale.•The waterfall module can obtain delicate local representations. Aiming at the problem of developing efficient models for human pose estimation algorithms under computation-limited resources. In this paper, we proposed an efficient high-resolution network for human pose estimation, which named FastNet. First of all, The Lite-HRNet was constructed by using four parallel subnetworks. Each subnetwork contained multiple bottleneck block for feature extraction. The Lite-HRNet can effectively learn the high-resolution representation features of key points because of maintaining high-resolution. Secondly, instead of standard convolution, the asymmetric convolution was introduced to build an asymmetric bottleneck module. The asymmetric bottleneck module has different aspect ratios, and can be used to exact image features of keypoints with multi-scale characteristics and reduce the number of parameters. Thirdly, the waterfall module composed of multiple parallel convolutions were proposed to aggregates features with the same spatial size which can efficiently obtain delicate local representations. It retains rich spatial information and results in precise keypoint localization. Finally, the bottleneck blocks were replaced with asymmetric bottleneck modules in the third subnetwork of LiteHRNet. By which, The waterfall module is embedded into the structure of FastNet. Comprehensive experiments demonstrate that the proposed method achieves superior results on two benchmark datasets, MSCOCO and MPII. Moreover, FastNet demonstrates superior results on human pose estimation over popular lightweight networks.