Morphological residual convolutional neural network(M-RCNN)for intelligent recognition of wear particles from artificial joints

作     者：Xiaobin HU Jian SONG Zhenhua LIAO Yuhong LIU Jian GAO Bjoern MENZE Weiqiang LIU Xiaobin HU;Jian SONG;Zhenhua LIAO;Yuhong LIU;Jian GAO;Bjoern MENZE;Weiqiang LIU

作者机构：Department of Computer ScienceTechnical University of MunichGarching 85748Germany School of Biomedical EngineeringSun Yat-sen UniversityGuangzhou 510006China Key Laboratory of Biomedical Materials and Implant DevicesResearch Institute of Tsinghua University in ShenzhenShenzhen 518057China State Key Laboratory of TribologyTsinghua UniversityBeijing 100084China Mckelvey School of EngineeringWashington University in Saint LouisSt.LouisMO 63130USA 

出 版 物：《Friction》 (摩擦（英文版）)

年 卷 期：2022年第10卷第4期

页      面：560-572页

核心收录：

学科分类：08[工学] 0817[工学-化学工程与技术] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0802[工学-机械工程] 0801[工学-力学（可授工学、理学学位）] 

基　　金：This work is financially supported by the National Natural Science Foundation of China(No.51875303) Support through the start-up foundation from Sun Yat-sen University is also gratefully acknowledged.Xiaobin Hu acknowledges the funding from the China Scholarship Council(CSC). 

主　　题：wear particles classifier morphological priors data augmentation deep residual network 

摘      要：Finding the correct category of wear particles is important to understand the tribological behavior.However,manual identification is tedious and time-consuming.We here propose an automatic morphological residual convolutional neural network(M-RCNN),exploiting the residual knowledge and morphological priors between various particle types.We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution.Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance.M-RCNN demonstrates a much higher accuracy(0.940)than the deep residual network(0.845)and support vector machine(0.821).This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints.