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Deep CNN and twin support vector machine based model for detecting potholes in road network

International Journal on Smart Sensing and Intelligent Systems
Volume 18 (2025): Issue 1 (January 2025)
By:
Open Access
|Aug 2025

Figures & Tables

Figure 1:

Illustrates the proposed pothole detection model based on deep CNN and TSVM. CNN, convolutional neural network; TSVM, twin support vector machine.
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.
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.
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.
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.
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.

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.
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.
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.
Depicts the AUC of proposed deep CNN-TPSVM model and other existing techniques. CNN, convolutional neural network; TPSVM, twin parametric support vector machine.

Depicts the simulation results of the proposed deep CNN-TPSVM model and other existing models

TechniqueAccuracy (%)Recall (%)Precision (%)F1-score (%)
ANN79.7387.3285.8286.56
SVM82.6088.8388.0190.31
VGG1684.4390.5888.8290.99
VGG1987.6591.9791.5593.87
InceptionV390.0692.5594.8795.69
DBN model93.0494.3096.3196.92
Proposed deep CNN-TPSVM Model96.1298.1397.7997.96
DOI: https://doi.org/10.2478/ijssis-2025-0099 | Journal eISSN: 1178-5608
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Language: English
Submitted on: Apr 14, 2025
Published on: Aug 11, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
pothole detection,
twin support vector machine,
road infrastructure,
deep convolutional network
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2025 Mohit Misra, Saptarshi Gupta, Shailesh Tiwari, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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