煤矿井下人体姿态的快速估计是井下作业智慧安全检测的重要前提。为解决煤矿井下多尘多雾、照明不足及颜色相融等问题，提高人体姿态估计关键点分配准确度及网络运行速度，研究新的Optimising HigherHRNet(OH-HRNet)快速网络模型：对HigherHRNet模型的轻量化设计、关键点分配进行深入研究，提出了基于注意力机制的记忆卷积模块及强化骨骼约束的关键点分配算法，并改进了算法的损失函数。在煤矿井下场景数据集和COCO公开数据集上的实验结果表明: OH-HRNet在GPU的速度上是LitePose的1.06倍，平均精度均值mAP提高了7.4%，平均召回率均值mAR提高了14.0%，可以实现更有效的智慧安全检测。

Fast estimation of human pose in underground coal mine is an important prerequisite for intelligent safety detection of underground operation. Aiming at the problems of dusty and foggy, insufficient illumination and color blending in underground coal mine, this study conducts an in-depth study on the lightweight design and key point assignment of the HigherHRNet model and proposes a new Optimising HigherHRNet(OH-HRNet) fast network model in order to improve the accuracy of the key point assignment for human pose estimation as well as the network operation speed.The OH-HRNet model proposes a memory convolution module based on attention mechanism and a key point assignment algorithm with reinforced skeletal constraints, and the loss function of the algorithm is improved. Experiments on the coal mine underground scenario dataset and COCO public dataset show that, OH-HRNet is 1.06 times faster than LitePose in terms of GPU speed, with a 7.4% increase in mAP and a 14.0% increase in mAR, which can achieve more effective intelligent safety detection.