CNN Based Image Classification of Malicious UAVs

Article


Brown, Jason, Gharineiat, Zahra and Raj, Nawin. 2023. "CNN Based Image Classification of Malicious UAVs." Applied Sciences. 13 (1), pp. 1-13. https://doi.org/10.3390/app13010240
Article Title

CNN Based Image Classification of Malicious UAVs

ERA Journal ID211776
Article CategoryArticle
AuthorsBrown, Jason, Gharineiat, Zahra and Raj, Nawin
Journal TitleApplied Sciences
Journal Citation13 (1), pp. 1-13
Article Number240
Number of Pages13
Year2023
PublisherMDPI AG
ISSN2076-3417
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).

KeywordsUAV; drone; image classification; Convolutional Neural Networks
Article Publishing Charge (APC) Amount Paid0.0
Article Publishing Charge (APC) FundingOther
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020400702. Automation engineering
461103. Deep learning
460304. Computer vision
Byline AffiliationsSchool of Engineering
School of Surveying and Built Environment
School of Mathematics, Physics and Computing
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Brown, Jason. 2020. "An examination of the Skills Framework for the Information Age (SFIA) version 7." International Journal of Information Management. 51. https://doi.org/10.1016/j.ijinfomgt.2019.102058
Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction
Ghimire, Sujan, Deo, Ravinesh C., Raj, Nawin and Mi, Jianchun. 2019. "Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction." Renewable and Sustainable Energy Reviews. 113, pp. 1-19. https://doi.org/10.1016/j.rser.2019.109247
Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms
Ghimire, Sujan, Deo, Ravinesh C., Raj, Nawin and Mi, Jianchun. 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms." Applied Energy. 253, pp. 1-20. https://doi.org/10.1016/j.apenergy.2019.113541
Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction
Ghimire, Sujan, Deo, Ravinesh C., Raj, Nawin and Mi, Jianchun. 2019. "Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction." Energies. 12 (12), pp. 1-42. https://doi.org/10.3390/en12122407
Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia
Ghimire, Sujan, Deo, Ravinesh C., Downs, Nathan J. and Raj, Nawin. 2019. "Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia." Journal of Cleaner Production. 216, pp. 288-310. https://doi.org/10.1016/j.jclepro.2019.01.158
Blocking analysis of persistent resource allocations for M2M applications in wireless systems
Brown, Jason, Afrin, Nusrat and Khan, Jamil. 2016. "Blocking analysis of persistent resource allocations for M2M applications in wireless systems ." Transactions on Emerging Telecommunications Technologies. 27 (11), pp. 1513-1529. https://doi.org/10.1002/ett.3091
ICT skill frameworks: do they achieve their goals and users’ expectations?
Brown, Jason and Parr, Alan. 2018. "ICT skill frameworks: do they achieve their goals and users’ expectations?" Advanced Journal of Professional Practice. 1 (2), pp. 38-47.
Predictive resource allocation in the LTE uplink for event based M2M applications
Brown, Jason and Khan, Jamil. 2013. "Predictive resource allocation in the LTE uplink for event based M2M applications." Kim, Dong-In and Mueller, Peter (ed.) IEEE International Conference on Communications (ICC 2013): Bridging the Broadband Divide. Budapest, Hungary 09 - 13 Jun 2013 Piscataway, NJ. United States. https://doi.org/10.1109/ICCW.2013.6649208
Key performance aspects of an LTE FDD based Smart Grid communications network
Brown, Jason and Khan, Jamil. 2013. "Key performance aspects of an LTE FDD based Smart Grid communications network." Computer Communications. 36 (5), pp. 551-561. https://doi.org/10.1016/j.comcom.2012.12.007
Delay models for static and adaptive persistent resource allocations in wireless systems
Brown, Jason, Afrin, Nusrat and Khan, Jamil Y.. 2016. "Delay models for static and adaptive persistent resource allocations in wireless systems." IEEE Transactions on Mobile Computing. 15 (9), pp. 2193-2205. https://doi.org/10.1109/TMC.2015.2492546
A predictive resource allocation algorithm in the LTE uplink for event based M2M applications
Brown, Jason and Khan, Jamil. 2015. "A predictive resource allocation algorithm in the LTE uplink for event based M2M applications." IEEE Transactions on Mobile Computing. 14 (12), pp. 2433-2446. https://doi.org/10.1109/TMC.2015.2398447
Description and assessment of regional sea-level trends and variability from altimetry and tide gauges at the northern Australian coast
Gharineiat, Zahra and Deng, Xiaoli. 2018. "Description and assessment of regional sea-level trends and variability from altimetry and tide gauges at the northern Australian coast." Advances in Space Research. 61 (10), pp. 2540-2554. https://doi.org/10.1016/j.asr.2018.02.038
Coastal altimetry for sea level changes in Northern Australian coastal oceans
Gharineiat, Zahra. 2017. Coastal altimetry for sea level changes in Northern Australian coastal oceans. PhD Thesis Doctor of Philosophy. University of Newcastle.
Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model
Deo, Ravinesh C., Ghimire, Sujan, Downs, Nathan J. and Raj, Nawin. 2018. "Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model." Kim, Dookie, Roy, Sanjiban Sekhar, Lansivaara, Tim, Deo, Ravinesh C. and Samui, Pijush (ed.) Handbook of research on predictive modeling and optimization methods in science and engineering. Hershey, United States. IGI Global. pp. 328-359
Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region
Mouatadid, Soukayna, Raj, Nawin, Deo, Ravinesh C. and Adamowski, Jan F.. 2018. "Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region." Atmospheric Research. 212, pp. 130-149. https://doi.org/10.1016/j.atmosres.2018.05.012
Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities
Ghimire, Sujan, Deo, Ravinesh C., Downs, Nathan J. and Raj, Nawin. 2018. "Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities." Remote Sensing of Environment: an interdisciplinary journal. 212, pp. 176-198. https://doi.org/10.1016/j.rse.2018.05.003
Adiabatic decay of internal solitons due to Earth’s rotation within the framework of the Gardner–Ostrovsky equation
Obregon, Maria, Raj, Nawin and Stepanyants, Yury. 2018. "Adiabatic decay of internal solitons due to Earth’s rotation within the framework of the Gardner–Ostrovsky equation." Chaos: an interdisciplinary journal of nonlinear science. 28 (3), pp. 1-11. https://doi.org/10.1063/1.5021864
Observing and modelling the high water level from satellite radar altimetry during tropical cyclones
Deng, Xiaoli, Gharineiat, Zahra, Andersen, Ole B. and Stewart, Mark G.. 2016. "Observing and modelling the high water level from satellite radar altimetry during tropical cyclones." Rizos, Chris and Willis, Pascal (ed.) 2013 IAG Scientific Assembly. Postdam, Germany 01 - 06 Sep 2013 Switzerland. https://doi.org/10.1007/1345_2015_108
Adiabatic decay of internal solitons in a rotating ocean
Obregon, M. A., Raj, N. and Stepanyants, Y. A.. 2016. "Adiabatic decay of internal solitons in a rotating ocean." 20th Australasian Fluid Mechanics Conference (AFMC 2016). Perth, Australia 05 - 08 Dec 2016 Australia.
Application of the multi adaptive regression splines to integrate sea level data from altimetry and tide gauges for monitoring extreme sea level events
Gharineiat, Zahra and Deng, Xiaoli. 2015. "Application of the multi adaptive regression splines to integrate sea level data from altimetry and tide gauges for monitoring extreme sea level events." Marine Geodesy. 38 (3), pp. 261-276. https://doi.org/10.1080/01490419.2015.1036183
Nonlinear vector waves of a flexural mode in a chain model of atomic particles
Nikitenkova, S. P., Raj, N. and Stepanyants, Y. A.. 2015. "Nonlinear vector waves of a flexural mode in a chain model of atomic particles." Communications in Nonlinear Science and Numerical Simulation. 20 (3), pp. 731-742. https://doi.org/10.1016/j.cnsns.2014.05.031
UICC control over devices used to obtain service
Brown, Jason and Ahluwalia, Inderpreet Singh. 2014. UICC control over devices used to obtain service. US8639290B2
Nonlinear spectra of shallow water waves
Giovanangeli, J. -P., Kharif, C., Raj, N. and Stepanyants, Y.. 2013. "Nonlinear spectra of shallow water waves." Oceans - San Diego, 2013. San Diego, United States 23 - 26 Sep 2013 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.23919/OCEANS.2013.6741132
Numerical study of nonlinear wave processes by means of discrete chain models
Obregon, M., Raj, N. and Stepanyants, Y.. 2012. "Numerical study of nonlinear wave processes by means of discrete chain models." Gu, Y. T. and Saha, Suvash C. (ed.) 4th International Conference on Computational Methods (ICCM 2012). Gold Coast, Australia 25 - 28 Nov 2012 Brisbane, Australia.
Multiple subscription subscriber identity module (SIM) card
Brown, Jason. 2009. Multiple subscription subscriber identity module (SIM) card. US7613480B2