# CNN Based Image Classification of Malicious UAVs
## 4 Conclusions and Further Work  
In this paper, we have described the collection of an image dataset for 3 popular UAVs at different elevations, different distances from the observer, and using different camera zoom levels. This UAV image dataset has been modelled using four CNN image classification algorithms, comprising AlexNet, VGG-16, Resnet-18 and MobileNet v2. The accuracy of the trained models on previously unseen test images is up to approximately 81% (for Resnet-18). This is encouraging given that two of the UAVs, the DJI Mavic 2 Enterprise and the DJI Mavic Air, are visually similar, and given that some of the photos of UAVs were taken at relatively large distances from the observer. The main anticipated application of this work is the automatic identification of the manufacturer and specific product identification of a UAV contained in an image which is part of a malicious UAV report. However, it could equally be employed in real time by security cameras (e.g., on buildings or other infrastructure) which identify an unwanted or even illegal UAV in the vicinity.  
The main limitations of this image classification based technique for UAV identification are (1) it can be difficult to distinguish between UAVs which are of similar appearance and/or have similar flight characteristics, and (2) UAVs which are far from the observer/camera will appear small in the image, thus complicating identification via image classification. Therefore, it may be useful to combine this technique with other methods of UAV identification (such as radar or acoustic signature), although this may not always be feasible depending upon the scenario.  
Given the encouraging results to date, we plan to expand the image dataset to include more UAV types, and more UAV usage scenarios, e.g., UAVs carrying loads, UAVs in motion and UAVs that are part of swarms. In addition, all the images collected to date were taken from below the UAV, because the main application for the work is an observer manually observing a (malicious) UAV from the ground. However, there is also the possibility that the images can be captured from above the UAV, e.g., by security cameras on tall buildings or even by another UAV. Therefore, we also plan to take photos of UAVs from above. The set of candidate image classification models may also be expanded; given the anticipated increased size of the image database, more complex models such as Resnet-34 may be considered. Finally, we would also like to expand the work to include object detection of UAV type in video streams using a YOLO variant as the object detection algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **VGG-16** & **Resnet-18** & **AlexNet** & **MobileNet v2** \\ \hline Mean & 184.1 s & 113.7 s & 68.0 s & 78.5 s \\ \hline Median & 184.0 s & 120.0 s & 67.0 s & 78.5 s \\ \hline \hline \end{tabular}
\end{table}
Table 7: Training Times for Various Models.  
**Author Contributions:** Conceptualization, J.B., Z.G. and N.R.; methodology, J.B. and Z.G.; software, J.B.; validation, J.B., Z.G. and N.R.; data curation, J.B. and Z.G.; writing--original draft preparation, J.B.; writing--review and editing, J.B., Z.G. and N.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ongoing data collection and management.  
**Conflicts of Interest:** The authors declare no conflict of interest.