DL Based Multi-Class Drone Classification for Counter Drone Detection Applications
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 11 |
| Year of Publication : 2023 |
| Authors : Venkata Subba Rao Pittu, Usha Rani Nelakuditi, Pavani Bandla, Yojitha Thotakura |
10.14445/23488379/IJEEE-V10I11P102
How to Cite?
Venkata Subba Rao Pittu, Usha Rani Nelakuditi, Pavani Bandla, Yojitha Thotakura, "DL Based Multi-Class Drone Classification for Counter Drone Detection Applications," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 11, pp. 21-30, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I11P102
Abstract:
Drones are the new disruption technology at present owing to their use in many areas like transport, agriculture, security, surveillance, surveying & mapping, etc., to handle various critical tasks with less complexity and cost-effectively. In this research, drone usage in air surveillance, especially as a counter drone technique, is considered which is a significant threat today at borders. Transfer learning-based multiclass drone detection and classification were implemented using pretrained ResNet-50, VGG16, Inception and Xception nets. Drone detection and classification performance for drone, bird, helicopter, and aeroplane classes are validated using accuracy, precision, F-score and recall metrics. Xception net is performing well over other nets with an accuracy of 0.98
Keywords:
UAV, Transfer learning, VGG16, ResNet50, Inception net, Xception net, Drone detection, Drone classification.
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