Lightweight Stacked Hourglass Network for Efficient Robotic Arm Pose Estimation
Qi Wang, Liaomo Zheng, Shiyu Wang, Xinjun Liu · 2021 7th International Conference on Computer and Communications (ICCC) · 2021
Existing robotic arm pose estimation methods usually only focus on improving the accuracy of the model, but ignoring model efficiency, therefore the development of models with a large number of parameters and computation in practical use. To solve the problem, this paper proposes a new HetConv-Based Stacked Hourglass Network (HetConv-SHN). HetConv-SHN develops from the Stacked Hourglass Network (SHN), which constructs an SHN with two stacks, and compresses the model by replacing the standard convolution in the SHN with a lightweight HetConv (Heterogeneous Kernel-Based Convolution) module. Experiments conducted on four typical datasets demonstrate that HetConv-SHN can achieve better accuracy than its counterparts with fewer parameters and computation. It can be better deployed on robotic arms, enabling lightweight robotic arm pose estimation.