In human pose detection task， the existing deep learning networks have the problems of insufficient detection precision， complex network parameters and high computational cost， which seriously limit their applications. To solve these problems， a lightweight and high-precision enhancement pose detection network HG-YOLO （High-precision and Ghost YOLO） was proposed. Aiming at the problem of insufficient detection precision， the Transformer-based detection network RT-DETR （Real-Time DEtection TRansformer） was integrated into the backbone part of HG-YOLO， and the Large Separable Kernel Attention （LSKA） module was embedded into the backbone， which improved feature extraction ability of the network to cope with the complex scenarios without increasing the memory occupation and computational complexity， thus improving the human pose detection precision. Aiming at the problem of complex network parameters and high computational cost， the lightweight Ghost convolution module was introduced to replace some of the standard convolutions， and furthermore， a shared convolution detection head was designed in the detection head part of HG-YOLO， which reduced the convolution computation through the parameter and weight sharing mechanism， thus reducing number of parameters and computational complexity of the network. Experimental results on the COCO （Common Objects in Context） 2017-Keypoints dataset and the CrowdPose dataset show that compared to the benchmark YOLOv8-Pose network， HG-YOLO reduces the parameters by 32% and the floating-point operations by 18%； and when the scale is s （small）， on the COCO 2017-Keypoints dataset， HG-YOLO has the AP50 （Average Precision at OKS （Object Keypoint Similarity） of 0.50） improved by 0.8 percentage points， on the CrowdPose dataset， HG-YOLO has the AP improved by 2.9 percentage points. It can be seen that HG-YOLO is not only lightweight but also has high detection precision， which is an excellent network model in the field of human pose detection.