面向变电站近电作业的高精度多人姿态估计方法

A high-precision multi-human body pose estimation approach for near-electricity work in substations

在变电站近电作业场景中,人体姿态估计对于准确定位人体关键点信息至关重要。然而,由于肢体或设备的遮挡,传统检测方法常常面临精度低、漏检和误检等问题。为此,提出一种面向变电站近电作业的高精度多人姿态估计方法。首先,在骨干网络中嵌入DCN(可变形卷积网络),使模型能够自主学习人体关节特征,并增强了几何建模能力。其次,构建一个基于ConvNeXt v2 Block的特征金字塔网络作为颈部结构,通过跨尺度连接方式加强特征间的交互学习。在预测头中引入CA(协调注意力机制),以进一步捕获特征图的通道和方向信息。最后,通过改进原损失函数,加速了模型的收敛速度。结果表明,与基准模型相比,所提模型的平均检测精度P_(0.50)、P_(0.75)和P分别提高了2.7%、7.3%、4.2%,可为变电站复杂环境下近电作业人员的安全提供重要的技术支撑。

Accurate human pose estimation is crucial for precisely locating key points of human body during the near-electricity work in substations.However,traditional detection methods often suffer from low accuracy,missed detections,and misdetections due to occlusion by limb or equipment.To address these challenges,the paper pro⁃poses a high-precision multi-human body pose estimation method tailored for near-electricity work in substations.First,a deformable convolutional network(DCN)is embedded into the backbone network,enabling the model to autonomously learn human joint features and enhancing its geometric modeling capabilities.Second,a feature pyra⁃mid network is constructed based on the ConvNeXt v2 Block as the neck structure.This strengthens feature interac⁃tion learning through cross-scale connections.In the prediction head,the coordinate attention(CA)mechanism is introduced to further capture channel and spatial information of feature maps.Finally,by improving the original loss function,the model’s convergence speed is accelerated.The results show that,compared to the baseline model,the proposed model’s average detection accuracies P_(0.50),P_(0.75),and P have increased by 2.7%,7.3%,and 4.2%,respectively.This provides significant technical support for the safety of near-electricity workers in complex substation environments.