Analyzing Identification Friend or Foe (IFF) of Armored Vehicles: A Comparative Approach with Transfer Learning and Pre-Trained Models
DOI:
https://doi.org/10.37266/ISER.2025v12i1.pp53-69Keywords:
Deep Transfer Learning, Pre-Trained Deep Learning Models, Target Classification, AI EthicsAbstract
In military operations, the proper use of force in accordance with the laws of war are non-negotiable. As the battlespace is becoming more and more volatile, uncertain, complex, and ambiguous, military leaders are becoming more dependent upon advanced technologies such as artificial intelligence to assist in their decision making. One such area that military leaders need advanced technology support is positive control of all friendly assets as well as identification of enemy and noncombatant assets in all domains of air, land, sea, and space through the identification of friend or foe (IFF). The purpose of this study is to assess the viability of deep transfer learning to assist in military decision-making and answer the research question: Can pre-trained deep learning models be used to adequately identify and classify enemy targets? This study is designed as a comparative study to assess and compare various pre-trained deep-learning models to determine if they are adequate for targeting and engaging the enemy. An ensemble model was also incorporated using three pretrained models and compared to the results of the individual models. A discussion of Human-in-the-loop concepts as well as the ethical considerations of the use of AI for IFF is incorporated in this study.
References
Abaimov, S., & Martellini, M. (2020). Artificial Intelligence in Autonomous Weapon Systems. In M. Martellini & R. Trapp (Eds.), 21st Century Prometheus: Managing CBRN Safety and Security Affected by Cutting-Edge Technologies (pp. 141–177). Springer International Publishing. https://doi.org/10.1007/978-3-030-28285-1_8
Alhudhaif, A., Saeed, A., Imran, T., Kamran, M., S. Alghamdi, A., O. Aseeri, A., & Alsubai, S. (2022). A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification. Computer Systems Science and Engineering, 40(1), 223–235. https://doi.org/10.32604/csse.2022.018430
Ali, L., Alnajjar, F., Jassmi, H. A., Gocho, M., Khan, W., & Serhani, M. A. (2021). Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures. Sensors, 21(5), 1688. https://doi.org/10.3390/s21051688
Amoroso, D. and Tamburrini, G. (2020). Autonomous Weapons Systems and Meaningful Human Control: Ethical and Legal Issues. Current Robotics Reports 1, no. 4 (December 1, 2020): 187–94. https://doi.org/10.1007/s43154-020-00024-3.
Bhatt, A., & Ganatra, A. (2022). Explosive Weapons and Arms Detection with Singular Classification (WARDIC) on Novel Weapon Dataset using Deep Learning: Enhanced OODA Loop. Engineered Science, Volume 20 (December 2022), 252–266. https://doi.org/10.30919/es8e718
Bezdan, T., & Bačanin Džakula, N. (2019). Convolutional Neural Network Layers and Architectures. Proceedings of the International Scientific Conference - Sinteza 2019, 445–451. https://doi.org/10.15308/Sinteza-2019-445-451
Billing, D. C., Fordy, G. R., Friedl, K. E., & Hasselstrøm, H. (2021). The implications of emerging technology on military human performance research priorities. Journal of Science and Medicine in Sport, 24(10), 947–953. https://doi.org/10.1016/j.jsams.2020.10.007
Clark, M., Allen, C., Keegan, T., Meinhart, R., Wong, L., & Reed, G. (2010). Strategic leadership primer (p. 0071). S. J. Gerras (Ed.). Department of Command, Leadership and Management, US Army War College. https://doi.org/10.1037/e660342010-001
Davis, S. I. (2022). Artificial intelligence at the operational level of war. Defense & Security Analysis, 38(1), 74–90. https://doi.org/10.1080/14751798.2022.2031692
Defense Security Cooperation Agency. (2018). “Chapter 3 | Defense Security Cooperation Agency.” Accessed October 14, 2023. https://samm.dsca.mil/chapter/chapter-3.
Diggelen, J, van den Bosch, K., Neerincx, M., and Steen, M. Designing for Meaningful Human Control in Military Human-Machine Teams. arXiv, May 12, 2023. https://doi.org/10.48550/arXiv.2305.11892.
Elder, H., Shank, D., Canfield, C., Rieger, T., & Hines, C. (2022). Knowing When to Pass: The Effect of AI Reliability in Risky Decision Contexts. Human factors, https://doi.org/10.1177/00187208221100691
“Ethic Noun – Definition, Pictures, Pronunciation and Usage Notes | Oxford Advanced American Dictionary at OxfordLearnersDictionaries.Com.” Accessed October 11, 2023. https://www.oxfordlearnersdictionaries.com/us/definition/american_english/ethic.
Euromaidan Press. “Ukraine Uses Drones Equipped with AI to Destroy Military Targets,” October 6, 2023. https://euromaidanpress.com/2023/10/06/ukraine-uses-drones-equipped-with-ai-to-destroy-military-targets/.
Feng, S., Ji, K., Zhang, L., Ma, X., & Kuang, G. (2021). SAR Target Classification Based on Integration of ASC Parts Model and Deep Learning Algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10213–10225. https://doi.org/10.1109/JSTARS.2021.3116979
Gilman, D., Nichols, R., & Totman, P. J. C. (2014). Foreign military sales. Defense Security Cooperation Agency. http://www. dsca. mil/sites/default/files/final-fms-dcs_30_sep. pdf.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (arXiv:1704.04861). arXiv. http://arxiv.org/abs/1704.04861
Ismail, N. S. A., & Benlahcene, A. (2018). A Narrative Review Of Ethics Theories: Teleological & Deontological Ethics. IOSR Journal Of Humanities And Social Science (IOSR-JHSS). 23, 31–38. https://doi.org/10.9790/0837-2307063138
Janakiramaiah, B., Kalyani, G., Karuna, A., Prasad, L. V. N., & Krishna, M. (2021). Military object detection in defense using multi-level capsule networks. Soft Computing, 27(2), 1045–1059. https://doi.org/10.1007/s00500-021-05912-0
Jug, J., Lampe, A., Štruc, V., & Peer, P. (2022). Body Segmentation Using Multi-task Learning (arXiv:2212.06550). arXiv. http://arxiv.org/abs/2212.06550
Kania, E., (2019). Chinese Military Innovation in the AI Revolution. The RUSI Journal. 164(5-6), 26-34. https://doi.org/10.1080/03071847.2019.1693803
Kant, I. Groundwork for the Metaphysics of Morals. Edited by Thomas E. Hill and Arnulf Zweig. New York: Oxford University Press, 1785.
Kong, L., Wang, J., & Zhao, P. (2022). YOLO-G: A Lightweight Network Model for Improving the Performance of Military Targets Detection. IEEE Access, 10, 55546–55564. https://doi.org/10.1109/ACCESS.2022.3177628
Layton, P. (2021). Fighting Artificial Intelligence Battles Operational Concepts for Future AI-Enabled Wars.
Lesinski, G., Corns, S. M., & Dagli, C. H. (2016). A fuzzy genetic algorithm approach to generate and assess meta-architectures for non-line of site fires battlefield capability. 2016 IEEE Congress on Evolutionary Computation (CEC), 2395–2401. https://doi.org/10.1109/CEC.2016.7744085
Li, H., Yu, L., Zhang, J., & Lyu, M. (2022). Fusion Deep Learning and Machine Learning for Heterogeneous Military Entity Recognition. Wireless Communications and Mobile Computing, 2022, e1103022. https://doi.org/10.1155/2022/1103022
Lin, Z., Ye, H., Zhan, B., & Huang, X. (2020). An Efficient Network for Surface Defect Detection. Applied Sciences, 10(17), 6085. https://doi.org/10.3390/app10176085
Liu, Y., Yu, Y., Wang, L., Nyima, T., Zhaxi, N., Huang, H., & Deng, Q. (2020). Classification of Tank Images Using Convolutional Neural Network. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 210–214. https://doi.org/10.1109/ITNEC48623.2020.9085151
Löhr, G. “If Conceptual Engineering Is a New Method in the Ethics of AI, What Method Is It Exactly?” AI and Ethics, May 16, 2023. https://doi.org/10.1007/s43681-023-00295-4.
Ma, Q., Li, S., & Cottrell, G. W. (2022). Adversarial Joint-Learning Recurrent Neural Network for Incomplete Time Series Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4), 1765–1776. https://doi.org/10.1109/TPAMI.2020.3027975
Majumder, U. K., Blasch, E. P., & Garren, D. A. (2020). Deep Learning for Radar and Communications Automatic Target Recognition. Artech House.
Mission Command. (n.d.). Retrieved April 27, 2024, from https://www.moore.army.mil/Infantry/DoctrineSupplement/ATP3-21.8/appendix_b/DirectFireControl/FireControlMeasures/index.html
Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2023). Human-in-the-loop machine learning: A state of the art. Artificial Intelligence Review, 56(4), 3005–3054. https://doi.org/10.1007/s10462-022-10246-w
Neupane, D., & Seok, J. (2020). A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition. Electronics, 9(11), Article 11. https://doi.org/10.3390/electronics9111972
Oh, J., & Kim, M. (2021). PeaceGAN: A GAN-Based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifier. Remote Sensing, 13(19), Article 19. https://doi.org/10.3390/rs13193939
Oveis, A. H., Giusti, E., Ghio, S., & Martorella, M. (2022). Moving and Stationary Targets Separation in SAR Signal Domain Using Parallel Convolutional Autoencoders with RPCA Loss. 2022 IEEE Radar Conference (RadarConf22), 1–6. https://doi.org/10.1109/RadarConf2248738.2022.9764168
Özmen, M., Aksoy, B. (2023), An Example Application for An Identification of Friend and Foe (IFF) System Appropriate for Unmanned Aerial Vehicles (UAV) Based on Deep Learning. J Intell Robot Syst 107, 36 (2023). https://doi.org/10.1007/s10846-023-01840-3
Schwartz, P. J., O’Neill, D. V., Bentz, M. E., Brown, A., Doyle, B. S., Liepa, O. C., Lawrence, R., & Hull, R. D. (2020). AI-enabled wargaming in the military decision making process. Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 11413, 118–134. https://doi.org/10.1117/12.2560494
Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition (arXiv:1409.1556). arXiv. http://arxiv.org/abs/1409.1556
Taddeo, M., & Blanchard, A. (2023). A Comparative Analysis of the Definitions of Autonomous Weapons. In F. Mazzi (Ed.), The 2022 Yearbook of the Digital Governance Research Group (pp. 57–79). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-28678-0_6
Tamboli, S., Jagadale, K., Mandavkar, S., Katkade, N., & Ruprah, T. S. (2023). A Comparative Analysis of Weapons Detection Using Various Deep Learning Techniques. 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), 1141–1147. https://doi.org/10.1109/ICOEI56765.2023.10125710
Transfer learning and fine-tuning | TensorFlow Core. (2024). TensorFlow. Retrieved April 27, 2024, from https://www.tensorflow.org/tutorials/images/transfer_learning
United States Army Maneuver Center of Excellence (MCoE). (2024). Infantry Platoon and Squad (ATP 3-21.8). Retrieved from https://armypubs.army.mil/epubs/DR_pubs/DR_a/ARN40007-ATP_3-21.8-000-WEB-1.pdf.
U.S. Department of Defense. (2023). Department of Defense Law of War Manual.
U.S. Department of Defense. (2020, February 24). Ethical principles for artificial intelligence – ai.mil. Ethical Principles for Artificial Intelligence. https://www.ai.mil/docs/Ethical_Principles_for_Artificial_Intelligence.pdf
Vincze, V. (2020). The USS Vincennes incident: A case study involving Autonomous Weapon Systems. Honvédségi Szemle – Hungarian Defence Review, 148(2), Article 2. 92-101. https://doi.org/10.35926/HDR.2020.2.6
When to use Convolutional Neural Networks (CNN)?. OpenGenusIQ. Retrieved March 26, 2024, from https://iq.opengenus.org/when-to-use-convolutional-neural-network-cnn/
Wu, J., Huang, Z., Hu, Z., & Lv, C. (2023). Toward Human-in-the-Loop AI: Enhancing Deep Reinforcement Learning via Real-Time Human Guidance for Autonomous Driving. Engineering, 21, 75–91. https://doi.org/10.1016/j.eng.2022.05.017
Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., & He, L. (2022). A survey of human-in-the-loop for machine learning. Future Generation Computer Systems, 135, 364–381. https://doi.org/10.1016/j.future.2022.05.014
Zhang, Y., Dai, Z., Zhang, L., Wang, Z., Chen, L., & Zhou, Y. (2020). Application of Artificial Intelligence in Military: From Projects View. 2020 6th International Conference on Big Data and Information Analytics (BigDIA), 113–116. https://doi.org/10.1109/BigDIA51454.2020.00026
Zhao, B., Wang, C., Fu, Q., & Han, Z. (2021). A Novel Pattern for Infrared Small Target Detection With Generative Adversarial Network. IEEE Transactions on Geoscience and Remote Sensing, 59(5), 4481–4492. https://doi.org/10.1109/TGRS.2020.3012981
Zhuang, X., Li, D., Wang, Y., & Li, K. (2024). Military target detection method based on EfficientDet and Generative Adversarial Network. Engineering Applications of Artificial Intelligence, 132, 107896. https://doi.org/10.1016/j.engappai.2024.107896
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