For human pose estimation, a high-score representation method is usually adopted for detecting key points; however, this detection is difficult to achieve because of numerous network parameters and complicated calculations. In this study, to realize a closer connection between layers and achieve an enhanced lightweight nature, the densely connected network (DenseNet) is employed and densely connected layers are proposed. The network calculation parameters are reduced while the detection accuracy is maintained, and the network computing speed is optimized. Second, a fusion method that combines upsampling and deconvolution modules in the multiscale fusion stage is proposed, facilitating more abundant output feature information and more accurate detection results more accurate. Finally, the COCO 2017 and MPII datasets are used for validating the proposed method. Experimental results show that compared with other human pose estimation algorithms, the proposed method achieves an average network accuracy of 74.8%, reduces the number of operating parameters by 63.8%, and decreases the network calculation complexity by 8.5% while ensuring the accuracy of real-time effects.