A Deep Learning Approach to Distance Map Generation Applied to Automatic Fiber Diameter Computation from Digital Micrographs
Abstract
:1. Introduction
- We describe our approach to distance map generation using deep neural network models (which is the first such work to the best of our knowledge).
- To facilitate the model training, we used sets of real and simulated fiber micrographs, because it is possible to obtain synthetic and realistic digital micrographs.
- To optimize network learning, three different loss functions are used.
- We introduce an experimental protocol designed for future reference, ensuring transparent and equitable comparisons.
2. Related Work
3. Materials and Methods
3.1. Overall Network Architecture
3.2. CNN Base Architectures
3.3. CNN Framework
3.4. Loss Function
3.5. Distance Map Label
3.6. Datasets
3.6.1. Real Micrographs
3.6.2. Synthetic Micrographs
4. Experimental Results
4.1. Evaluation Protocol
- The micrographs from the first and third groups (G1 and G3) are used for training and cross-validation.
- The second and fourth groups (G2 and G4) are used for testing.
4.2. Experiments
4.3. Implementation
4.4. Computational Time
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
CNN | Convolutional Neural Network |
MAE | Mean Absolute Error |
MSE | Mean Squared Error |
OFDA | Optical Fibre Diameter Analyzer |
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Group | Purpose | Database * | Images |
---|---|---|---|
G1 | Training | 1 | 43 |
G2 | Testing | 1 | 11 |
G3 | Training | 2 | 43 |
G4 | Testing | 2 | 11 |
Model | Loss Function | MAE | MSE |
---|---|---|---|
U-Net Regression | L1 Loss | 0.1094 | 0.0882 |
L2 Loss | 0.1319 | 0.0711 | |
Smooth L1 Loss | 0.1137 | 0.0759 | |
SkeletonNet Regression | L1 Loss | 0.2041 | 0.2880 |
L2 Loss | 0.3087 | 0.2473 | |
Smooth L1 Loss | 0.2592 | 0.2519 |
Model | Loss Function | MAE | MSE |
---|---|---|---|
U-Net Regression | L1 Loss | 0.2268 | 0.1958 |
L2 Loss | 0.2467 | 0.1229 | |
Smooth L1 Loss | 0.2139 | 0.1726 | |
SkeletonNet Regression | L1 Loss | 0.2755 | 0.2717 |
L2 Loss | 0.3223 | 0.1892 | |
Smooth L1 Loss | 0.3093 | 0.2851 |
Model | Time per Image [s] | Images per Second |
---|---|---|
U-Net Regression | 0.077 | 12.987 |
SkeletonNet Regression | 0.094 | 10.638 |
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Alejo Huarachi, A.M.; Beltrán Castañón, C.A. A Deep Learning Approach to Distance Map Generation Applied to Automatic Fiber Diameter Computation from Digital Micrographs. Sensors 2024, 24, 5497. https://doi.org/10.3390/s24175497
Alejo Huarachi AM, Beltrán Castañón CA. A Deep Learning Approach to Distance Map Generation Applied to Automatic Fiber Diameter Computation from Digital Micrographs. Sensors. 2024; 24(17):5497. https://doi.org/10.3390/s24175497
Chicago/Turabian StyleAlejo Huarachi, Alain M., and César A. Beltrán Castañón. 2024. "A Deep Learning Approach to Distance Map Generation Applied to Automatic Fiber Diameter Computation from Digital Micrographs" Sensors 24, no. 17: 5497. https://doi.org/10.3390/s24175497