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

Abstract

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 proposes 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 pyramid network is constructed based on the ConvNeXt v2 Block as the neck structure. This strengthens feature interaction 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 P0.50， P0.75， 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.

Keywords

• near-electricity work
• human body pose estimation
• yolo v7
• dcn v2 module
• attention mechanism