# CNN Based Image Classification of Malicious UAVs
## 1 Introduction
However, our paper does not address UAVs carrying loads, and a future direction of our research will be to complement the image dataset with images of UAVs carrying loads. In [9], the authors trained a You Only Look Once (YOLO) model to detect and track UAVs in video streams. They used the DJI Mavic Pro and DJI Phantom III for validation purposes and achieved a mean average precision (mAP) of 74.36%, which was superior to previous studies. However, they did not distinguish whether the UAV in the video stream was a DJI Mavic Pro or DJI Phantom III, identifying it only as a generic UAV. The objective of our research is different, to be able to distinguish between different UAV types (e.g., by manufacturer and specific model) based upon an image provided. The study in [10] employed a dataset of 506 images and 217 audio samples to train and test a deep-learning model for the detection of UAVs based upon combined visual and audio characteristics. The best accuracy obtained was 98.5%. However, the aim was not to differentiate between different types of UAVs as in our research; rather, it was to distinguish between UAVs as a general class of object and other objects such as airplanes, birds, kites, and balloons. The combined video/audio approach was also adopted in [11], but despite using different types of UAV such as a DJI Phantom 4 and DJI Mavic in the training and testing of the model, they did not attempt to distinguish the exact UAV type in the vicinity of the observer; instead, the objective was simply to detect that a UAV of some type was present. The research presented in this paper is different from that reviewed above and novel in that it attempts to classify the manufacturer and specific product identification of a UAV in an image, with the UAV being at various elevations and distances from the observer, and with the observer using various zoom levels when taking the photo. The task is challenging both because we are attempting to distinguish between UAVs which may look similar, especially from a distance, and because the image may be taken at different distance and viewing angles with respect to the target UAV. Before we captured our own dataset, we investigated whether any other public domain-specific datasets [12; 13] existed in this area. In terms of other image and/or video datasets that could potentially be employed as part of this study, most public datasets are of images and/or videos taken by drones/UAVs rather than images and/or videos of drones/UAVs. One notable exception is a dataset of 1359 UAV images on Kaggle [14]. However, this dataset cannot be meaningfully used in our study for a variety of reasons (1) many of the images are not of UAVs in flight, rather they are images of people holding a UAV or the UAV on the ground, (2) there is no information on the manufacturer or model of the UAV in the metadata, (3) the dataset is not balanced in terms of an equal number of images of each UAV type, and (4) for those images representing UAVs in flight, there is no information on elevation or horizontal distance from the camera. The novel contributions of the paper are as follows:  
* Development of a methodology to capture a balanced and structured image dataset of different UAVs in flight. The dataset is balanced in terms of the number of images of each UAV and structured in that images are captured at specified elevations, horizontal distances and zoom levels. There is no similar (public) image dataset available. * Training, testing and cross validation of various deep learning image classification models to be able to distinguish the manufacturer and specific product identification of a UAV in an image. * An analysis of the ability of the image classification models to distinguish between UAVs which are extremely similar in visual appearance, and to classify UAVs which are distant from the observer.