关键词: 姿态检测, HG-YOLO, 大型可分离核注意力, 共享卷积检测头, 轻量化网络。

Abstract: In the human posture 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. In order to solve these problems, lightweight and highly precise improved network HG-YOLO(High-accuracy and Ghost YOLO) wasis proposed in this paper. To address the problem of insufficient detection precision, the Transformer-based detection network RT-DETR(Real-Time Detection Transformer) was integrated into the backbone part of the HG-YOLO network, and the Large Separable Kernel Attention (LSKA) module was embedded into the backbone network, which improved the feature extraction ability of the network to cope with the complex scenarios without increasing the memory occupation and computational complexity, thus improving the human body posture detection precision. To address the problem of complex network parameters and high computational cost, HG-YOLO introduced lightweight Ghost convolution module to replace some of the standard convolutions, and furthermore, a shared convolution detection head was designed in the detection head part of the HG-YOLO network, which reduced the convolution computation through the parameter and weight sharing mechanism, thus reducing the number of parameters and computational complexity of the network. Experimental results on the COCO 2017-Keypoints dataset and the CrowdPose dataset show that compared to the benchmark YOLOv8-Pose network, HG-YOLO reduces the amount of parameters by 32% and the amount of floating-point operations by 18%, and on the COCO 2017-Keypoints dataset, the AP50(Average Precision at OKS=0.50) improves by 1.8 percentage points, and on the CrowdPose dataset, the AP improves by 2.9 percentage points. It proves that the HG-YOLO network proposed in this paper is not only lightweight but also has high detection precision, which is an excellent network model in the field of human posture detection.

Key words: pose estimation, HG-YOLO, Large Separable Kernel Attention(LSKA), shared convolutional detection head, lightweight network.