Lightweight human pose estimation based on adaptive feature sensing

For the problems of complex network structure design, large number of model parameters and low detection efficiency in the existing human pose estimation network pursues high-precision detection, this paper proposes a lightweight human pose estimation algorithm based on adaptive feature perception. Firstly, the lightweight Ghost module is used to reconstruct the feature extraction network of human pose estimation to reduce the computation amount of the network. Secondly, a lightweight adaptive feature sensing attention mechanism is designed to reduce the complexity of network model and enhance the effective communication between channels, which can improve the positioning effect of key points. Finally, Huber Loss (Exponential square loss function) is used to optimize the loss function training model to achieve better prediction of outliers and enhance the robustness of the model. Verified on the COCO dataset, the experimental results show that compared with the benchmark RMPE algorithm, the detection accuracy of the improved model is increased by about 0.5%, the number of parameters is reduced by 56.0%, the network calculation amount is reduced by 32.6%, the model volume is compressed by about 57.0%, and the model detection rate is increased by about 2.1 times. In this paper, the improved human pose estimation model improves the detection efficiency and enhances the robustness of the model while compressing the model volume.