医疗卫生装备2024，Vol.45Issue(6)：1-8,8.DOI:10.19745/j.1003-8868.2024104
基于多级联深度学习处理器的机器人手术器械检测和姿态估计算法研究
Research on robot-based surgical instrument detection and pose estimation algorithm with multi-cascade deep learning processor
韩思齐 1陈敏葵 2魏丽璞 1冉骞 3徐谦 2余明 1孙玉超 4陈锋1
作者信息
- 1. 军事科学院系统工程研究院,天津 300161
- 2. 联勤保障部队第910医院骨科,福建泉州 362000
- 3. 联勤保障部队第910医院耳鼻喉科,福建泉州 362000
- 4. 天津理工大学中环信息学院,天津 300380
- 折叠
摘要
Abstract
Objective To propose a multi-cascade deep learning processor-based surgical instrument detection and pose estimation algorithm to facilitate the robotic scurb nurse to recognize and delivery surgical instruments.Methods The proposed multi-cascade deep leaning processor-based CYSP algorithm was hibernated with several functional modules such as YOLOX with coordinate attention block(CA-YOLOX),segment anything model(SAM)and principal component analysis(PCA).Firstly,CA-YOLOX was applied to identifying the types of the surgical instruments and completing the coarse positioning of x and y coordinates;secondly,the SAM segmenter was used to clarify the positions of the instruments in the RGB image,and the depth information and internal parameters of the camera were introduced to obtain the point cloud of the surgical instruments;finally,the center of mass,principal direction and normal direction of the surgical instrument point cloud were determined through the PCA algorithm,with which the rotation and translation(RT)matrix between the target coordinate system(surgical instrument center of mass coordinate system)and the base coordinate system of the robotic arm was solved,and the matrix was converted into a quaternion and then transmitted to the robotic arm control unit so as to drive the robotic arm to arrive at the corresponding position and pick up the instrument to complete the instrument delivery task.Migration training was accomplished on a self-constructed surgical instrument image dataset and the effectiveness of the proposed algorithm was evaluated,and instrument delivery experiments were performed on a seven-degree-of-freedom robotic arm and the success rate of the algorithm was assessed.Results The multi-cascade deep leaning processor-based CYSP algorithm had a recognition accuracy of 98.52％on the surgical instrument dataset,a success rate of 94％for the in-strument delivery experiment and average time for recognition of 0.28 s.Conclusion The multi-cascade deep leaning proces-sor-based CYSP algorithm with high reliability and practicability behaves well in facilitating the robotic scurb nurse to recog-nize and deliver surgical instruments.[Chinese Medical Equipment Journal,2024,45(6):1-8]关键词
器械护士机器人/深度学习/手术器械检测/姿态估计/坐标注意力机制Key words
robotic scurb nurse/deep learning/surgical instrument detection/pose estimation/coordinate attention mecha-nism分类
医药卫生引用本文复制引用
韩思齐,陈敏葵,魏丽璞,冉骞,徐谦,余明,孙玉超,陈锋..基于多级联深度学习处理器的机器人手术器械检测和姿态估计算法研究[J].医疗卫生装备,2024,45(6):1-8,8.
