Ensemble Deep Learning for Wear Particle Image Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Procedure, Data Acquisition and Augmentation
2.2. Deep Learning
2.2.1. Inception V3
2.2.2. Xception
2.2.3. MobileNetV2
2.2.4. Transfer Learning and Fine-Tuning
2.2.5. Ensemble Learning
3. Results and Discussions
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Deep Learning Model | Number of Parameters | Depth |
---|---|---|
Inception V3 | 23.8 Million | 159 |
Xception V2 | 22.9 Million | 71 |
MobileNet V2 | 3.4 Million | 53 |
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Shah, R.; Sridharan, N.V.; Mahanta, T.K.; Muniyappa, A.; Vaithiyanathan, S.; Ramteke, S.M.; Marian, M. Ensemble Deep Learning for Wear Particle Image Analysis. Lubricants 2023, 11, 461. https://doi.org/10.3390/lubricants11110461
Shah R, Sridharan NV, Mahanta TK, Muniyappa A, Vaithiyanathan S, Ramteke SM, Marian M. Ensemble Deep Learning for Wear Particle Image Analysis. Lubricants. 2023; 11(11):461. https://doi.org/10.3390/lubricants11110461
Chicago/Turabian StyleShah, Ronit, Naveen Venkatesh Sridharan, Tapan K. Mahanta, Amarnath Muniyappa, Sugumaran Vaithiyanathan, Sangharatna M. Ramteke, and Max Marian. 2023. "Ensemble Deep Learning for Wear Particle Image Analysis" Lubricants 11, no. 11: 461. https://doi.org/10.3390/lubricants11110461