Algorithms for Vision-Based Quality Control of Circularly Symmetric Components
Abstract
:1. Introduction
2. Materials and Methods
2.1. Image Preprocessing
2.1.1. Detection of the Specimen
2.1.2. Identification of the Object Center
2.1.3. Polar to Cartesian Coordinate Transformation
- is the horizontal coordinate of the original cartesian reference system;
- is the vertical coordinate of the original cartesian reference system;
- is the radius of the polar reference system;
- is the angle of the polar reference system;
2.2. Standard Approach
2.2.1. Features Extraction
- is the pseudo-signal describing the grey-scale level along a certain group of rows
- is the value of the th sample of the pseudo signal, corresponding to a specific horizontal position/angle;
- M is the height of the stripe expressed in pixels;
- is the lower coordinate of the stripe;
- is the upper coordinate of the stripe.
2.2.2. Feature-Based Classification
2.3. Deep Learning Approach
3. Case Study
3.1. Standard Approach
3.2. Deep Learning Approach
3.3. Performance Evaluation Procedure
- TP is the number of true positive classified samples;
- TN is the number of true negative classified samples;
- FP is the number of false positives classified as samples;
- FN is the number of false negative classified samples.
4. Results
- The time for the pre-processing routine common for the two approaches is 191.9 ± 9.8 ms (C.I. 95%), computed over the 200 samples of Washers’ training and validation split.
- The time for the computation of the pseudo signal is lower than 2 ms; a similar value is required to extract the teeth for the DL approaches.
- The time for the feature-based classification of a washer using the standard algorithm is lower than 1 ms for both the SVM classifier and the NN-based approach.
- The time for the classification of a tooth is 13.28 ± 0.06 ms (C.I. 95%) for the MobileNetV2 model and 36.71 ± 0.08 ms (C.I. 95%) for the ResNet50, both values were computed on 4500 samples of the teeth test dataset.
5. Discussion and Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
Abbreviation | Definition |
AI | Artificial Intelligence |
CSC | Circularly Symmetric Component |
DL | Deep Learning |
K | Kurtosis |
k-NN | k-Nearest-Neighbors |
MAF | Mean Angular Frequency |
MDAF | Median Angular Frequency |
ML | Machine Learning |
MLP | Multilayer Perceptron |
NN | Neural Network |
P2C | Polar to Cartesian |
RMS | Root Mean Square |
ROI | Region of Interest |
SD | Standard Deviation |
SK | Skewness |
SVM | Support Vector Machine |
Appendix A
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Model | MobileNetV2 | ResNet50 |
---|---|---|
Number of epochs | 125 | 400 |
Learning rate | 0.0001 | 0.00005 |
Batch size | 32 | 64 |
Model | SVM | NN |
---|---|---|
Accuracy | 0.97 | 0.98 |
Precision | 0.95 | 0.98 |
Recall | 0.97 | 0.98 |
Model | MobileNetV2 | ResNet50 |
---|---|---|
Accuracy | 0.997 | 0.997 |
Precision | 0.928 | 0.907 |
Recall | 0.975 | 0.992 |
Model | MobileNetV2 | ResNet50 |
---|---|---|
Accuracy | 0.890 | 0.890 |
Precision | 0.796 | 0.822 |
Recall | 0.975 | 0.925 |
Actual Class | Picture | MoblieNetV2 | ResNet50 | |
---|---|---|---|---|
(a) | Compliant | Compliant | Defective | |
(b) | Compliant | Compliant | Defective | |
(c) | Defective | Compliant | Defective | |
(d) | Defective | Compliant | Defective |
Algorithm | N° of Epochs | Training Time (One Epoch) | Total Training Time |
---|---|---|---|
Standard algorithm—SVM | -- | -- | <1 s |
Standard algorithm—NN | 20 | <1 s | 2 s |
DL—MobileNetV2 | 125 | 13 s | 27 min |
DL—ResNet50 | 400 | 33 s | 220 min |
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Brambilla, P.; Conese, C.; Fabris, D.M.; Chiariotti, P.; Tarabini, M. Algorithms for Vision-Based Quality Control of Circularly Symmetric Components. Sensors 2023, 23, 2539. https://doi.org/10.3390/s23052539
Brambilla P, Conese C, Fabris DM, Chiariotti P, Tarabini M. Algorithms for Vision-Based Quality Control of Circularly Symmetric Components. Sensors. 2023; 23(5):2539. https://doi.org/10.3390/s23052539
Chicago/Turabian StyleBrambilla, Paolo, Chiara Conese, Davide Maria Fabris, Paolo Chiariotti, and Marco Tarabini. 2023. "Algorithms for Vision-Based Quality Control of Circularly Symmetric Components" Sensors 23, no. 5: 2539. https://doi.org/10.3390/s23052539