多部位皮肤检测研究及其在洗浴机器人中的应用  

Multi-part skin detection and its application in bathing robots

作　　者：李平 喻洪流[1] LI Ping;YU Hongliu(Institute of Intelligent Rehabilitation Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Department of Biomedical Engineering,Changzhi Medical College,Changzhi 046000,China)

机构地区：[1]上海理工大学智能康复工程研究院,上海200093 [2]长治医学院生物医学工程系,长治046000

出　　处：《上海理工大学学报》2025年第1期1-8,29,共9页Journal of University of Shanghai For Science and Technology

基　　金：国家重点研发计划资助项目(2022YFC3601403);国家自然科学基金资助项目(62073224);山西省高等学校科技创新项目(2022L376);长治医学院博士科研启动基金项目(2024BS12)。

摘　　要：为了应对人口老龄化带来的挑战,并使洗浴机器人能够针对不同皮肤部位采用适当的洗浴模式,对多部位皮肤检测及其在洗浴机器人中的应用进行了研究。在前期研究的基础上,选取了4种典型目标检测算法,扩充了原始数据集,并基于迁移学习进行多部位皮肤检测。建立了综合评价指标以评估算法性能,在Tesla T4和TX2平台上对性能最佳的模型进行部署和测试,并将其应用于洗浴机器人中。结果显示:数据集类不平衡的改善可使检测精度平均提升18%;YOLOv5s算法在精度与模型大小之间达到了最佳平衡,能够在Tesla T4和TX2平台上进行实时检测,并在水汽环境中实现对不同部位皮肤的识别。通过TX2平台集成视觉传感器,进行目标点三维位姿建模和联合实验,控制机器人到达背部区域的成功率为92%,使用点云作为监督信息可将此成功率提升至100%。改善类不平衡可以显著提升多部位皮肤检测的准确性,YOLOv5s在平衡精度和模型大小方面表现出色,有效满足了洗浴机器人多部位皮肤检测的需求。To address the challenges posed by an aging population and to enable bathing robots to apply appropriate cleaning modes for different skin regions,the research was conducted on multi-part skin detection and its application in these robots.Building on previous studies,four representative object detection algorithms were selected,the original dataset was expanded,and multi-part skin detection was performed using transfer learning.A comprehensive evaluation metric was developed to assess algorithm performance,and the best-performing model was deployed and tested on Tesla T4 and TX2 platforms,with practical applications in bathing robots.The results showed that addressing dataset class imbalance lead to an average accuracy improvement of 18%.YOLOv5s has achieved an optimal balance between accuracy and model size,facilitating real-time detection on both Tesla T4 and TX2,and effectively identifying different skin regions in a humid environment.By integrating a visual sensor with TX2,3D pose modeling and joint experiments were conducted,achieving a 92%success rate in controlling the robot to reach the back area.Utilizing point cloud data as supervision improve the success rate to 100%.Addressing class imbalance significantly enhances the accuracy of multi-part skin detection,and YOLOv5s exhibites excellent performance in balancing accuracy and model size,effectively meeting the multi-part skin detection requirements of bathing robots.

关 键 词：皮肤检测 洗浴机器人 YOLOv5s算法 迁移学习 TX2平台 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]