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 convo...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.展开更多
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).
文摘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.
