High-Precision Hand Pose Estimation Based on Small Object Augmented Feature Pyramid

作者：Xie Qingjiang, Zhou Linglong, Geng Yujuan, Zhu Zhousen, Liao Xuehua · 发表于：Computer Science and Technology · 年份：2026 · DOI：10.57237/j.cst.2026.01.001 · 研究领域：Human Pose and Action Recognition、Hand Gesture Recognition Systems、Robot Manipulation and Learning

Hand joint pose assessment is a critical step in the forensic identification of range of motion, holding significant importance in the field of forensic science. To overcome the limitations of traditional methods that rely heavily on professional physicians, this paper proposes an improved hand pose assessment method based on computer vision. Focusing on 21 hand keypoints, the method addresses challenges such as their small size in images, susceptibility to occlusion, and limited feature information by constructing an optimized detection framework. Specifically, a Small Object Augmented Feature Pyramid is designed to alleviate the difficulty of losing small keypoint features; the SPDConv module is introduced to preserve detailed features; and the Omni-Frequency-Spatial Attention Kernel (Omni-FSAK) module is incorporated to achieve efficient multi-scale feature fusion, thereby significantly enhancing the perception capability for blurred, occluded, and tiny keypoints. In terms of detection head design, in addition to the classification branch, bounding box regression branch, and keypoint prediction branch, we further introduce a Localization Quality Calibrator (LQC). This mechanism fully utilizes the outputs from the classification branch and regression distribution information to evaluate the reliability of prediction results. It effectively compensates for the insufficiency of classification confidence in accurately reflecting localization quality, enabling the model to outp...