SMG-YOLO: an efficient human pose estimation method

Abstract

Human pose estimation is a fundamental task in computer vision that aims to localize key human joints in images. Although you only look once (YOLO)-based pose estimation methods provide advantages in computational efficiency and deployment convenience, they still face challenges in complex backgrounds, occlusions, small-scale keypoints, and structural inconsistency among predicted joints. To address these issues, this paper presents SMG-YOLO, an enhanced YOLO-based pose estimation framework built upon Hyper-YOLO-Pose. The proposed model integrates three pose-oriented components: the selective boundary aggregation (SBA) module, the mixed aggregation network (MANet)-StarC module, and a GroupNorm-based pose detection head, namely group normalization (GN)-Pose. The SBA module is adopted to strengthen semantic-spatial feature interaction between high-level semantic features and low-level spatial cues. The MANet-StarC module incorporates the StarC unit into the MANet structure and combines nonlinear feature modulation with context anchor attention to enhance contextual feature representation. GN-Pose introduces GN into the pose prediction head to improve keypoint regression stability. Experiments on the MPII human pose dataset show that SMG-YOLO achieves 85.30% AP50 and 47.70% AP50:95, improving the Hyper-YOLO-Pose baseline by 2.30 and 2.40 percentage points, respectively. Additional ablation experiments further verify the complementary effects of the proposed components. Moreover, model-forward speed testing on an RTX 3060 GPU shows that SMG-YOLO achieves 49.1 FPS, indicating practical inference efficiency under the tested setting. These results demonstrate that SMG-YOLO provides a favorable accuracy–complexity trade-off for human pose estimation.