Figure 1:
Illustrates the proposed pothole detection model based on deep CNN and TSVM. CNN, convolutional neural network; TSVM, twin support vector machine.
Figure 2:
Proposed deep CNN-based feature extractor model. CNN, convolutional neural network.
Figure 3:
Depicts the confusion matrix of proposed deep CNN-TPSVM model. CNN, convolutional neural network; TPSVM, twin parametric support vector machine.
Figure 4:
Depicts the confusion matrix of other existing models.
Figure 5:
Comparative analysis of the results using proposed deep CNN-TPSVM model and existing models based on different performance parameters. CNN, convolutional neural network; TPSVM, twin parametric support vector machine.
Table 1:
Depicts the simulation results of the proposed deep CNN-TPSVM model and other existing models
| Technique | Accuracy (%) | Recall (%) | Precision (%) | F1-score (%) |
|---|---|---|---|---|
| ANN | 79.73 | 87.32 | 85.82 | 86.56 |
| SVM | 82.60 | 88.83 | 88.01 | 90.31 |
| VGG16 | 84.43 | 90.58 | 88.82 | 90.99 |
| VGG19 | 87.65 | 91.97 | 91.55 | 93.87 |
| InceptionV3 | 90.06 | 92.55 | 94.87 | 95.69 |
| DBN model | 93.04 | 94.30 | 96.31 | 96.92 |
| Proposed deep CNN-TPSVM Model | 96.12 | 98.13 | 97.79 | 97.96 |
Figure 6:
Demonstrates the accuracy rate of the proposed deep CNN-TPSVM model based on training and validation sets. CNN, convolutional neural network; TPSVM, twin parametric support vector machine.
Figure 7:
Demonstrates the loss rate of the proposed deep CNN-TPSVM model based on training and validation sets. CNN, convolutional neural network; TPSVM, twin parametric support vector machine.
Figure 8:
Depicts the AUC of proposed deep CNN-TPSVM model and other existing techniques. CNN, convolutional neural network; TPSVM, twin parametric support vector machine.