In view of the problem of low precision and slow training of heatmap regression network trained by mean square error (MSE) loss function used in traditional heatmap regression, the loss function Heatmap Wing Loss (HWing Loss) for heatmap regression is proposed in this thesis. In terms of different pixel values, the loss function has different loss function values, and the loss function gradient of foreground pixels is larger, which can make the network focus more on the foreground pixels and make the heatmap regression more accurate and faster. In line with the distribution characteristics of the heatmap, the keypoint inference method based on the Gaussian distribution is adopted in this thesis to reduce the quantization error when the heatmap infers the keypoints. By taking the two points as the basis, it constructs a new monocular pose estimation algorithm based on keypoint positioning. According to the experiments, in contrast with the algorithm using MSE Loss, the pose estimation algorithm using HWing Loss has a higher ADD(-S) accuracy rate, which reaches 88.8% on the LINEMOD dataset. Meanwhile, the performance is better than other recent pose estimation algorithms based on deep learning. The algorithm in this thesis can run at the fastest speed of 25 fps on RTX3080 GPU, in which the high speed and performance can be both embodied.