PW-YOLO-Pose: A Novel Algorithm for Pose Estimation of Power Workers

To address the detection challenges of keypoints, such as misdetections and omissions caused by backgrounds, occlusions, small targets, and extreme viewpoints in complex electrical power operation environments for power workers. This study proposes a 2D pose estimation algorithm for power workers based on YOLOv5s6-Pose: PW-YOLO-Pose. In this study, the detection rate of occluded keypoints is improved by embedding the Swin Transformer encoder in the top layer of the backbone network. The proposed BiFPN (a weighted bi-directional feature pyramid network) structure with a small target detection layer improves the detection rate of small target characters and the precision of their keypoints’detection. The keypoint regression precision is improved overall by using CA (coordinate attention) in the model neck and improving the bounding box regression loss function to Wise-IoU. The algorithmic model in this study demonstrates excellent detection and largely meets the real-time requirements on the proposed power worker pose estimation dataset in this study. The $mAP_{0.5}$ (The mean average precision when the threshold for object keypoint similarity is set to 0.5.) and $mAP_{0.5:0.95}$ are 93.35% and 64.75% respectively, which are 5.22% and 1.53% higher than the baseline model. The detection time of a single image is 21.3 ms, respectively. It can serve as a valuable theoretical foundation and reference for behavior recognition and state monitoring of power workers in intricate electrical power operation environments.