A Multi-Scale Attention-Enhanced Algorithm for Hand Keypoint Detection

Abstract

Hand keypoint detection is important for human – computer interaction and industrial process monitoring, but practical deployment on resource-constrained devices still faces challenges such as the trade-off between accuracy and efficiency, limited robustness in dynamic scenes, and sensitivity to occlusion. To address these issues, this paper presents a two-stage hand analysis framework that combines an optimized YOLOv5s detector with HRNet-based keypoint estimation. In the detection stage, the backbone is replaced with InceptionNeXt and an adapted MSA-CAM module is introduced to improve feature representation in cluttered industrial scenes while reducing computational cost. In the pose stage, HRNet is used to estimate 21 hand keypoints from detected hand regions. Experiments on multiple hand datasets show that the proposed detector achieves a favorable balance between accuracy and efficiency. In a discrete workshop packaging scenario, the overall system also supports action-sequence recognition and anomaly detection, achieving 96.3% recognition success in the topview setting and 95.0% in the front-view setting. These results demonstrate the practical value of the proposed framework for real-time industrial hand analysis.