A Lightweight Network for Human Pose Estimation Based on ECA Attention Mechanism

This paper presents a novel approach to address the problem of increasing the number of parameters in existing human posture estimation network models while trying to improve prediction accuracy. We propose a human posture estimation optimization network model, called BDENet, which is based on the high-resolution detection network (HRNet). BDENet incorporates a bottleneck structure and depth-separable convolution to reduce the number of parameters. Additionally, it introduces an efficient channel attention (ECA) lightweight attention mechanism to enhance accuracy. We evaluate the proposed model using the MSCOCO dataset and compare it with HRNet. The experimental results demonstrate that BDENet achieves a 41.4% reduction in the number of parameters compared to HRNet, while achieving a 0.6% increase in accuracy. These findings confirm that the network model proposed in this paper can effectively improve accuracy while reducing the number of parameters.