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Research Article | Open Access

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

Xiaobin HU1, Jian SONG2( ), Zhenhua LIAO3, Yuhong LIU4( ), Jian GAO5, Bjoern MENZE1, Weiqiang LIU3,4
Department of Computer Science, Technical University of Munich, Garching 85748, Germany
School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510006, China
Key Laboratory of Biomedical Materials and Implant Devices, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
Mckelvey School of Engineering, Washington University in Saint Louis, St. Louis, MO 63130, USA
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Abstract

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.

Keywords

data augmentationwear particles classifiermorphological priorsdeep residual network

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Friction
Volume 10 Issue 4,
April 2022
Pages 560-572
DOI: 10.1007/s40544-021-0516-2
Cite this article:
HU X, SONG J, LIAO Z, et al. Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints. Friction, 2022, 10(4): 560-572. https://doi.org/10.1007/s40544-021-0516-2

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Received: 17 December 2020
Revised: 18 March 2021
Accepted: 30 March 2021
Published: 05 April 2022
© The author(s) 2021

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