机电工程技术2025，Vol.54Issue(3)：114-120,7.DOI:10.3969/j.issn.1009-9492.2025.03.021
物理约束与神经网络混合驱动的水黾姿态估计方法
Water Strider Pose Estimation Method Driven by Hybrid of Physical Constraints and Neural Networks
摘要
Abstract
Accurate animal pose estimation serves as a crucial foundation for analysis of animal locomotory characteristics.Given that water striders primarily rely on their middle legs for locomotion,with these legs straddling the water-air interface during surface movements,pose estimation poses significant challenges,particularly in differentiating middle leg joints.To improve the accuracy of pose estimation under complex environments,a hybrid approach combining physical constraints and neural networks for water strider motion pose estimation is proposed.Specifically,DeepLabCut neural network is firstly utilized to predict the pixel coordinates of each joint in the water strider's middle legs within images.Subsequently,physical constraints are incorporated based on the unique characteristics of the middle leg joints,leveraging the binocular camera's capability to acquire depth and distance information to refine the pre-estimated coordinates from the neural network,yielding refined pose estimation data.By integrating the stereo calibration matrix,the two-dimensional pose estimates are transformed from both cameras into a three-dimensional space,achieving three-dimensional pose estimation of the water strider.Experimental validation in a tailored setup acquires a high precision up to 89.11%,which is about 10.3%higher than standalone neural network predictions,demonstrates the achievement of high-precision pose estimation,with the hybrid method of physical constraints and neural networks outperforming standalone neural network predictions in terms of accuracy.关键词
仿生机器人/姿态估计/神经网络/混合驱动Key words
biomimetic robot/pose estimation/neural network/hybrid drive分类
信息技术与安全科学引用本文复制引用
叶瑛歆,黄聪,朱文强..物理约束与神经网络混合驱动的水黾姿态估计方法[J].机电工程技术,2025,54(3):114-120,7.基金项目
山东省自然科学基金青年项目(ZR2021QE128) （ZR2021QE128）
山东省重点研发计划项目(2022CXGC010101) （2022CXGC010101）
