CNN Based Image Classification of Malicious UAVs
Article
Article Title | CNN Based Image Classification of Malicious UAVs |
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ERA Journal ID | 211776 |
Article Category | Article |
Authors | Brown, Jason, Gharineiat, Zahra and Raj, Nawin |
Journal Title | Applied Sciences |
Journal Citation | 13 (1), pp. 1-13 |
Article Number | 240 |
Number of Pages | 13 |
Year | 2023 |
Publisher | MDPI AG |
ISSN | 2076-3417 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/app13010240 |
Web Address (URL) | https://www.mdpi.com/2076-3417/13/1/240 |
Abstract | Unmanned Aerial Vehicles (UAVs) or drones have found a wide range of useful applications in society over the past few years, but there has also been a growth in the use of UAVs for malicious purposes. One way to manage this issue is to allow reporting of malicious UAVs (e.g., through a smartphone application) with the report including a photo of the UAV. It would be useful to able to automatically identify the type of UAV within the image in terms of the manufacturer and specific product identification using a trained image classification model. In this paper, we discuss the collection of images for three popular UAVs at different elevations and different distances from the observer, and using different camera zoom levels. We then train 4 image classification models based upon Convolutional Neural Networks (CNNs) using this UAV image dataset and the concept of transfer learning from the well-known ImageNet database. The trained models can classify the type of UAV contained in unseen test images with up to approximately 81% accuracy (for the Resnet-18 model), even though 2 of the UAVs represented in the UAV image dataset are visually similar, and the fact that the UAV image dataset contains images of UAVs that are a significant distance from the observer. This provides a motivation to expand the study in the future to include more UAV types and other usage scenarios (e.g., UAVs carrying loads). |
Keywords | UAV; drone; image classification; Convolutional Neural Networks |
Article Publishing Charge (APC) Amount Paid | 0.0 |
Article Publishing Charge (APC) Funding | Other |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400702. Automation engineering |
461103. Deep learning | |
460304. Computer vision | |
Byline Affiliations | School of Engineering |
School of Surveying and Built Environment | |
School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/v0968/cnn-based-image-classification-of-malicious-uavs
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