Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications
Abstract
:1. Introduction
2. Related Work
3. UAV Detection Methodology
3.1. Audio Feature Extraction
3.2. Visual Feature Extraction
3.3. Support Vector Machine (SVM)
4. Experimental Results
4.1. Image Dataset Description
4.2. Audio Dataset Description
4.3. Malicious UAV Detection with Hand-Crafted Descriptors
4.4. UAV Detection with CNNs
Algorithm 1.Localization Algorithm |
|
4.5. Detection Using Audio
4.6. Computational Time
4.7. Comparison with Present Detection Methods
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Descriptor | Linear | Gaussian | Polynomial |
---|---|---|---|
HOG [13] | 82.7% | 50.6% | 50.6% |
LBP [12] | 53.8% | 59.0% | 62.2% |
GLCM [15] | 74.4% | 72.4% | 73.1% |
CJLBP [16] | 75.6% | 50.6% | 50.0% |
NRLBP [18] | 50.6% | 51.3% | 50.0% |
LTrP [17] | 61.5% | 50.6% | 50.0% |
LETRIST [14] | 57.1% | 50.6% | 50.0% |
AlexNet [21] | |||
Kernel | Accuracy | Sensitivity | Specificity |
Linear | 97.4% | 98.7% | 96.3% |
Gaussian | 50.6% | 50.3% | 100.0% |
Polynomial | 97.4% | 100.0% | 95.1% |
Inceptionv3 [24] | |||
Kernel | Accuracy | Sensitivity | Specificity |
Linear | 95.5% | 93.8% | 97.3% |
Gaussian | 50.6% | 50.3% | 100.0% |
Polynomial | 63.5% | 100.0% | 57.8% |
ResNet-50 [22] | |||
Kernel | Accuracy | Sensitivity | Specificity |
Linear | 96.8% | 98.7% | 95.1% |
Gaussian | 50.6% | 50.3% | 100.0% |
Polynomial | 95.5% | 100.0% | 91.8% |
GoogLeNet [25] | |||
Kernel | Accuracy | Sensitivity | Specificity |
Linear | 95.5% | 96.1% | 944.9% |
Gaussian | 50.6% | 50.3% | 100.0% |
Polynomial | 96.8% | 98.7% | 95.1% |
VGG-19 [23] | |||
Kernel | Accuracy | Sensitivity | Specificity |
Linear | 96.8% | 97.4% | 96.2% |
Gaussian | 50.6% | 50.3% | 100.0% |
Polynomial | 93.6% | 97.2% | 90.5% |
MFCC [20] | |||
Kernel | Accuracy | Sensitivity | Specificity |
Linear | 81.7% | 85.0% | 75.0% |
Gaussian | 98.3% | 97.5% | 100.0% |
Polynomial | 63.3% | 94.7% | 48.8% |
LPCC [19] | |||
Kernel | Accuracy | Sensitivity | Specificity |
Linear | 65.0% | 100.0% | 65.0% |
Gaussain | 63.3% | 97.4% | 64.4% |
Polynomial | 83.3% | 86.7% | 82.2% |
Ref No. | Audio Data | Image Data | Sample Approach | Accuracy |
---|---|---|---|---|
[29] | √ | - | Deep Belief Network | 88.0% |
[36] | √ | - | Correlation | 70% |
[38] | √ | - | HMM | 81.3% |
[47] | √ | - | SVM with Genetic Algorithm | 95.0% |
This Paper | √ | - | MFCC | 98.3% |
[48] | - | √ | ResNet-50 | 96.8% |
[49] | - | √ | FD-HOG | 82.7% |
[50] | - | √ | LBP and HOG | 62.2% and 82.7% |
This Paper | - | √ | AlexNet | 97.4% |
[26] | √ | √ | HOG and MFCC | 82.7% and 98.3% |
This Paper | √ | √ | AlexNet and MFCC | 98.5% |
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Jamil, S.; Fawad; Rahman, M.; Ullah, A.; Badnava, S.; Forsat, M.; Mirjavadi, S.S. Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications. Sensors 2020, 20, 3923. https://doi.org/10.3390/s20143923
Jamil S, Fawad, Rahman M, Ullah A, Badnava S, Forsat M, Mirjavadi SS. Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications. Sensors. 2020; 20(14):3923. https://doi.org/10.3390/s20143923
Chicago/Turabian StyleJamil, Sonain, Fawad, MuhibUr Rahman, Amin Ullah, Salman Badnava, Masoud Forsat, and Seyed Sajad Mirjavadi. 2020. "Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications" Sensors 20, no. 14: 3923. https://doi.org/10.3390/s20143923
APA StyleJamil, S., Fawad, Rahman, M., Ullah, A., Badnava, S., Forsat, M., & Mirjavadi, S. S. (2020). Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications. Sensors, 20(14), 3923. https://doi.org/10.3390/s20143923