High-Precision Hand Pose Estimation Based on Small Object Augmented Feature Pyramid
1. School of Computer Science, Sichuan Normal University, Chengdu 610101, China
2. School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu 610101, China
| Abstract: | 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 output high-quality detection and keypoint prediction results more robustly under the same network scale. Experiments on the FreiHAND and KeypointsHand datasets verify the effectiveness of the proposed method. Compared with the baseline method, the accuracy is improved by 3.15% and the recall rate is increased by 1.74%, fully validating the feasibility of the improved scheme. |
| Keywords: | Hand Keypoint Detection; Small Object Detection; Forensic Identification; Pose Estimation; Localization Quality Estimation |
| DOI: | 10.57237/j.cst.2026.01.001 |
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Received: 24 November 2025
Accepted: 26 December 2025
Published Online: 14 January 2026
Funding
| 1. | 国家社会科学基金一般项目 (20BMZ092) |
References
| [1] | Kadam P, Fang G, Amirabdollahian F, et al. Hand Pose Detection Using YOLOv8-pose [C] // 2024 IEEE Conference on Engineering Informatics (ICEI). IEEE, 2024: 1-6. |
| [2] | Che W, Zhang H, Wu B, et al. SFG-YOLOv8: efficient and lightweight small-feature gesture keypoint detector [J]. Journal of King Saud University Computer and Information Sciences, 2025, 37(4): 44. |
| [3] | Myanganbayar B, Mata C, Dekel G, et al. Partially occluded hands: A challenging new dataset for single-image hand pose estimation [C] // Asian Conference on Computer Vision. Cham: Springer International Publishing, 2018: 85-98. |
| [4] | Jia X, Feng J, Liang B. Hand keypoint detection with super resolution [C] // Proceedings of the 7th International Conference on Cyber Security and Information Engineering. 2022: 398-401. |
| [5] | Romero J, Tzionas D, Black M J. Embodied hands: Modeling and capturing hands and bodies together [J]. arXiv preprint arXiv: 2201.02610, 2022. |
| [6] | JING L, WANG B. EMNet: Edge-guided multi-level network for salient object detection in low-light images [J]. Image and Vision Computing, 2024, 143: 104933. |
| [7] | 侯贝贝.基于手部姿态估计的手关节角度测量系统研究[D].中国科学院大学 (中国科学院西安光学精密机械研究所), 2024. https://doi.org/10.27605/d.cnki.gkxgs.2024.000060 |
| [8] | Chen M, Shuang F, Li S, et al. Robust dexterous hand control strategy cascading bare hand pose estimation and joint jitter suppression [J]. Robotics and Autonomous Systems, 2025: 105189. |
| [9] | 任建华,曹佳惠,贾迪.基于掩码提示和注意力的手部姿态估计[J/OL].计算机应用, 1-11 [2025-09-29]. https://doi.org/10.11772/j.issn.1001-9081.2024111715 |
| [10] | 郑泉石,金城.基于自适应预测的2D人体姿态估计[J].计算机科学, 2023, 50(S2): 162-168. https://doi.org/CNKI:SUN:JSJA.0.2023-S2-023 |
| [11] | 刘佳, 石豪, 陈大鹏,等. 基于YOLOv3-HM的手部姿态估计方法研究 [J]. 测控技术, 2023(004): 60-66, 87. |
| [12] | 倪广兴, 徐华, 王超. 融合改进YOLOv5及Mediapipe的手势识别研究 [J]. 计算机工程与应用, 2024, 60(07): 108-118. |
| [13] | Ding J, Niu S, Nie Z, et al. Research on human posture estimation algorithm based on YOLO-Pose [J]. Sensors, 2024, 24(10): 3036. |
| [14] | Zhu X, Lyu S, Wang X, et al. TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios [C] // Proceedings of the IEEE/CVF international conference on computer vision. 2021: 2778-2788. |
| [15] | Guan X, Shen H, Nyatega C O, et al. Repeated Cross-Scale Structure-Induced Feature Fusion Network for 2D Hand Pose Estimation [J]. Entropy, 2023, 25(5): 724. |
| [16] | Zimmermann C, Ceylan D, Yang J, et al. Freihand: A dataset for markerless capture of hand pose and shape from single rgb images [C] // Proceedings of the IEEE/CVF international conference on computer vision. 2019: 813-822. |
| [17] | Zhang Y, Du S, He H. Rotator-YOLOv5: Improved YOLOv5 for Vehicle and Vessel Detection in UAV Images [C] // 2024 Fourth International Conference on Digital Data Processing (DDP). IEEE, 2024: 156-161. |
| [18] | Zhao P. SPFFNet: Strip perception and feature fusion spatial pyramid pooling for fabric defect detection [J]. arXiv preprint arXiv: 2502.01445, 2025. |
| [19] | Yang G, Lei J, Zhu Z, et al. AFPN: Asymptotic feature pyramid network for object detection [C] // 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2023: 2184-2189. |
| [20] | 李少东, 罗凯, 黄远智, 等. 复杂交互场景下融合关节遮挡信息的手部姿态估计研究 [J]. 计算机学报, 2025, 48(05): 1212-1231. |
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