Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines
Abstract
:1. Introduction
- Implementation of the proposed fuzzy contrast enhancement (FCE) image preprocessing step for VGG19, Xception and SVM algorithms. Additionally, the performances of all algorithms without and with the proposed FCE were analyzed and compared.
- Creation of a novel RGB dataset of 4500 aerial images from a Primus Air Max small wind turbine mimicking the environment of a wind turbine farm using a small-scale wind turbine prototype to compare the performance of the three ML algorithms.
- Detailed analyses and implementation of the VGG19, Xception, and SVM algorithms using different optimization methods, model training, and hyperparameter tuning technologies.
2. Convolutional Neural Networks (CNNs)
2.1. VGG19
2.2. Xception
3. Support Vector Machine (SVM)
4. Proposed Image Preprocessing with Fuzzy Contrast Enhancement (FCE)
4.1. CIELAB Color Space
4.2. Fuzzification of Image Pixel Intensity
Algorithm 1 Fuzzy Logic-Based Image Contrast Enhancement |
Input: Aerial RGB image Output: Aerial RGB image with fuzzy contrast enhancement
|
5. Hyperparameter Tuning
5.1. Hyperparameter Categories
5.1.1. Optimizer
5.1.2. Activation Functions
5.1.3. Batch Size
5.1.4. Loss Function
5.1.5. Dropout
6. Experimental Results
6.1. Dataset Overview and Characteristics
6.2. VGG19 Experimental Results
6.2.1. VGG19 Case Study A1
6.2.2. VGG19 Case Study A2
6.2.3. VGG19 Case Study A3
6.2.4. VGG19 Case Study A4
6.2.5. VGG19 Case Study A5
6.3. Xception Experimental Results
6.3.1. Xception Case Study B1
6.3.2. Xception Case Study B2
6.3.3. Xception Case Study B3
6.3.4. Xception Case Study B4
6.3.5. Xception Case Study B5
6.3.6. Xception Case Study B6
6.4. SVM Experimental Results
6.4.1. SVM Case Study C1
6.4.2. SVM Case Study C2
6.4.3. SVM Case Study C3
6.4.4. SVM Case Study C4
7. Conclusions
- A comprehensive exploration, implementation, and comparison of three distinct machine learning algorithms, comprising two convolutional neural networks and the SVM algorithm. These algorithms are utilized to classify whether RGB images contain wind turbines.
- The application of the proposed fuzzy contrast enhancement (FCE) data preprocessing step to the VGG19, Xception, and SVM machine learning algorithms, accompanied by a comparison of their conventional performance against their performance when augmented by this preprocessing step.
- The creation of a novel Primus Air Max wind turbine classification dataset, consisting of 4500 RGB images, and its utilization to assess the performance of the implemented VGG19, Xception, and SVM algorithms.
- The assessed convolutional neural networks, namely Xception and VGG19, demonstrated commendable performances with accuracies above 98%. In contrast, as anticipated, the SVM algorithm exhibited a less favorable accuracy of around 90%.
- The implementation of the proposed FCE results in enhanced accuracy for Xception and SVM, while VGG19 does not experience similar improvements.
- The results presented in Figure 7 and Figure 8 and Table 2 and Table 3 demonstrate that implementing the proposed FCE leads to improvements in accuracy, precision, and F1 score. Specifically, for the Xception model, these metrics increase from 99%, 99.2%, and 98.99% to 99.18%, 99.5%, and 99.18%, respectively. Similarly, for the SVM algorithm, the application of FCE raises accuracy, precision, and F1 score from 90.3%, 91%, and 90.12% to 95.48%, 95.63%, and 95.48%, respectively.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Layer # | Layer Details | Layer # | Layer Details |
---|---|---|---|
1 | Conv (64) | 11 | Conv (512) |
2 | Conv (64) | 12 | Conv (512) |
- | MaxPool | - | MaxPool |
3 | Conv (128) | 13 | Conv (512) |
4 | Conv (128) | 14 | Conv (512) |
- | MaxPool | 15 | Conv (512) |
5 | Conv (256) | 16 | Conv (512) |
6 | Conv (256) | - | MaxPool |
7 | Conv (256) | 17 | Fully Connected (4096) |
8 | Conv (256) | 18 | Fully Connected (4096) |
- | MaxPool | 19 | Fully Connected (1000) |
9 | Conv (512) | - | SoftMax |
10 | Conv (512) | - | - |
Algorithm | Accuracy | Precision | Hit Rate | Miss Rate | Specificity | Fall-Out | F1 Score |
---|---|---|---|---|---|---|---|
Xception | 99.00% | 99.21% | 98.78% | 1.21% | 99.21% | 0.78% | 98.99% |
VGG19 | 98.26% | 98.34% | 98.16% | 1.83% | 98.36% | 1.64% | 98.25% |
SVM | 90.31% | 91.00% | 87.15% | 12.84% | 94.01% | 5.98% | 90.12% |
Algorithm | Accuracy | Precision | Hit Rate | Miss Rate | Specificity | Fall-Out | F1 Score |
---|---|---|---|---|---|---|---|
Xception | 99.18% | 99.50% | 98.84% | 1.15% | 99.51% | 0.49% | 99.17% |
VGG19 | 97.99% | 97.05% | 98.87% | 1.12% | 97.14% | 2.85% | 97.95% |
SVM | 95.48% | 95.63% | 94.75% | 5.25% | 96.23% | 3.76% | 95.48% |
Case Study | No. | BS | LR | DR | Validation Accuracy (VA) |
---|---|---|---|---|---|
Case Study A1 | 1 | 30 | 0.0005 | 30% | 98.36% |
2 | 60 | 0.0001 | 30% | 98.35% | |
3 | 30 | 0.001 | 20% | 98.32% | |
Case Study A2 | 1 | 20 | 0.0005 | 20% | 98.35% |
2 | 60 | 0.0005 | 30% | 98.30% | |
3 | 60 | 0.0001 | 20% | 98.29% | |
Case Study A3 | 1 | 300 | 0.0001 | 30% | 98.41% |
2 | 60 | 0.0001 | 20% | 98.39% | |
3 | 150 | 0.0001 | 20% | 98.25% |
Case Study | No. | BS | L | O | DL | DR | LR | B1 | B2 | E | AMSG | Accuracy |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Case B1 | 1 | 20 | BC | Adam | True | 60% | 0.9 | 0.999 | False | 95.321% | ||
2 | 5 | BC | Adam | True | 10% | 0.9 | 0.999 | False | 94.509% | |||
3 | 5 | BC | Adam | True | 80% | 0.9 | 0.999 | False | 94.209% | |||
Case B2 | 1 | 32 | BC | Adam | False | - | 0.9 | 0.999 | False | 88.953% | ||
2 | 32 | BC | Adam | False | - | 0.9 | 0.999 | False | 88.590% | |||
3 | 32 | BC | Adam | False | - | 0.9 | 0.999 | False | 88.462% | |||
Case B3 | 1 | 10 | BC | Adam | True | 45% | 0.9 | 0.99 | True | 93.686% | ||
2 | 20 | BC | Adam | True | 35% | 0.0 | 0.99 | True | 93.103% | |||
3 | 5 | BC | Adam | True | 25% | 0.0 | 0.0 | True | 89.103% | |||
Case B4 | 1 | 5 | BC | Adam | True | 45% | 0.0 | 0.0 | True | 96.058% | ||
2 | 10 | BC | Adam | True | 20% | 0.0 | 0.999 | False | 94.872% | |||
3 | 5 | BC | Adam | True | 20% | 0.9 | 0.9999 | True | 94.359% |
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Ward, Z.; Miller, J.; Engel, J.; Masoum, M.A.S.; Shekaramiz, M.; Seibi, A. Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines. Machines 2024, 12, 55. https://doi.org/10.3390/machines12010055
Ward Z, Miller J, Engel J, Masoum MAS, Shekaramiz M, Seibi A. Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines. Machines. 2024; 12(1):55. https://doi.org/10.3390/machines12010055
Chicago/Turabian StyleWard, Zachary, Jordan Miller, Jeremiah Engel, Mohammad A. S. Masoum, Mohammad Shekaramiz, and Abdennour Seibi. 2024. "Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines" Machines 12, no. 1: 55. https://doi.org/10.3390/machines12010055
APA StyleWard, Z., Miller, J., Engel, J., Masoum, M. A. S., Shekaramiz, M., & Seibi, A. (2024). Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines. Machines, 12(1), 55. https://doi.org/10.3390/machines12010055