Image partitioning with windowed and panoramic configuration for passive 360-degree camera in military unmanned ground vehicle: A machine learning-based detection framework
References
- Andersson, C. (2021). The unmanned ground vehicles to be used in future military operations. Tiede ja ase, 2021(79), 85–89 ISSN: 0358-8882
- Andersson, C. A., Halme, K., Laine, M., Hulkko, V., & Virtanen, K. (2024). Effectiveness of an expendable unmanned ground vehicle stalling a mechanized infantry company’s primary combat units – A virtual simulation experiment. Journal of Field Robotics, 42, pp. 1125–1142. doi: 10.1002/rob.22442
- Army Recognition. (2023a). New Unmanned Ground Vehicles UGV Produced in Russia – Defense News August 2023 Global Security Army Industry – Defense Security Global News Industry Army Year 2023 – Archive News Year –
armyrecognition.com . Available athttps://www.armyrecognition.com/defense_news_august_2023_global_security_army_industry/new_unmanned_ground_vehicles_ugv_produced_in_russia.html [accessed 23 July, 2024]. - Army Recognition. (2023b). Ukraine to Deploy Ironclad Articulated UGV in Combat Operations – Ukraine – Russia Conflict War 2022 – Analysis Focus Army Defence Military Industry Army –
armyrecognition.com . Available athttps://www.armyrecognition.com/focus-analysis-conflicts/army/conflicts-in-the-world/russia-ukraine-war-2022/ukraine-to-deploy-articulated-ironclad-ugv-in-combat-operations?highlight=WyJpcm9uY2xhZCJd [accessed 23 July, 2024]. - Askew, J. (2023). How the Ukraine War is Driving Technological Innovation –
euronews.com . Available athttps://www.euronews.com/next/2023/09/27/drones-and-robots-how-the-ukraine-war-is-driving-technological-innovation [accessed 23 July, 2024]. - Atherton, K. D. (2019). Beetle-Like Iranian Robots can Roll Under Tanks –
c4isrnet.com . Available athttps://www.c4isrnet.com/unmanned/2019/10/08/beetle-like-iranian-robots-roll-under-tanks/ [accessed 22 July, 2024]. - Baker, J. (2023). Ukraine Eyes up New ‘Funnel Web’ Missile-Launcher to Sneak behind Enemy Lines – thesun.co.uk. Available at
https://www.thesun.co.uk/news/23816980/missile-launching-robots-key-ukraine-war/ [accessed 22 July, 2024]. - Bureau. (2023). Ukraine, Russia Rush to Develop Ground Mobile Mines –
defensemirror.com . Available athttps://www.defensemirror.com/news/34093/Ukraine__Russia_Rush_to_Develop_Ground_Mobile_Mines [accessed 23 July, 2024]. - Demir, B., Ergunay, S., Nurlu, G., Popovic, V., Ott, B., Wellig, P., et al. (2020). Real-time high-resolution omnidirectional imaging platform for drone detection and tracking. Journal of Real-Time Image Processing, 17(5), pp. 1625–1635. doi: 10.1007/s11554-019-00921-7
- Gordon, A. (2022). Grounded in reality: Charting the Uncrewed Land Systems Market - Global Defence Technology – Issue 138 – December 2022 –
defence.nridigital.com . Available athttps://defence.nridigital.com/global_defence_technology_dec22/grounded_in_reality_charting_the_uncrewed_land_systems_market [accessed 23 July, 2024]. - Graswald, M., Gutser, R., Breiner, J., Grabner, F., Lehmann, T., & Oelerich, A. (2019). Defeating modern armor and protection systems. Hypervelocity Impact Symposium, Vol. 883556. American Society of Mechanical Engineers, p. V001T03A004.
- Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., et al. (2022). A survey on vision transformer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1), pp. 87–110. doi: 10.1109/TPAMI.2022.3152247
- Heikkila, H., Eskelinen, P., Hautala, P., & Ruoskanen, J. (2004). Upgrading armored vehicle sensor systems. IEEE Aerospace and Electronic Systems Magazine, 19(1), pp. 26–32. doi: 10.1109/MAES.2004.1263989
- Hemminki, P., Andersson, C., Nieminen, M., & Haapamäki, T. (2022). (In Finnish) Autonomia Taistelukentällä – Tulevaisuusorientoitunut Tutkimus. Puolustustutkimuksen Vuosikirja 2022.
- He, K., Zhang, X., Ren, S., & Sun, J. (2016). Identity mappings in deep residual networks. In: Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14. Springer, pp. 630–645.
- Jafarzadeh, P., Zelioli, L., Farahnakian, F., Nevalainen, P., Heikkonen, J., Hemminki, P., et al. (2023). Real-time military tank detection using YOLOv5 implemented on raspberry pi. In: 2023 4th International Conference on Artificial Intelligence, Robotics and Control (AIRC). IEEE, pp. 20–26. doi: 10.1109/AIRC57904.2023.10303260
- Joshi, H., & Katare, L. J. (2024). Unmanned Ground Vehicles (UGVs) Market Overview 2030, Size, Share, Growth, Industry, Growth –
marketresearchfuture.com . Available athttps://www.marketresearchfuture.com/reports/unmanned-ground-vehicles-market-10120 [accessed 23 July, 2024]. - Kaur, P., Khehra, B. S., & Mavi, E. B. S. (2021). Data augmentation for object detection: A review. In: 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, pp. 537–543.
- Kirill, R. (2022). Self-propelled mine Gnom Kamikaze: Ukrainian implementation of an unsuccessful concept –
eu.topwar.ru . Available athttps://en.topwar.ru/201057-samohodnaja-mina-gnome-kamikaze-ukrainskaja-realizacija-neudachnoj-koncepcii.html [accessed 23 July, 2024]. - Kosonen, P. (2020). Autonomous Robots Out in the Wild – A Software Engineering Challenge. Available at
https://medium.com/starshiptechnologies/running-autonomous-robots-on-city-streets-is-very-much-a-software-engineering-challenge-66927869090a [accessed 12 July, 2024]. - Kulchandani, J. S., & Dangarwala, K. J. (2015). Moving object detection: Review of recent research trends. In: 2015 International Conference on Pervasive Computing (ICPC). IEEE, pp. 1–5.
- Leiss, P. (2018). The Functional Components of Autonomous Vehicles. Available at
https://www.robsonforensic.com/articles/autonomous-vehicles-sensors-expert [accessed 12 July, 2024]. - Liang, S., Wang, W., Chen, R., Liu, A., Wu, B., & Chang, E. C., et al. (2024). Object detectors in the open environment: Challenges, solutions, and outlook. arXiv preprint arXiv:2403.16271.
- Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., & Fu, C. Y., et al. (2016). SSD: Single shot multibox detector. In: European Conference on Computer Vision, Springer. doi: 10.48550/arXiv.1512.02325.
- Lubenets, A. (2021). The Story Behind the Creation of Yandex’s Delivery Robot. Available at
https://medium.com/yandex-self-driving-car/the-story-behind-the-creation-of-yandexs-delivery-robot-e07017940589 [accessed 12 July, 2024]. -
Malyasov, D. (2023). Ukrainian Army Uses Drones as Remote Mine-Laying System –
defence-blog.com . Available athttps://defence-blog.com/ukrainian-army-uses-drones-as-remote-mine-laying-system/ [accessed 23 July, 2024].MalyasovD. 2023 Ukrainian Army Uses Drones as Remote Mine-Laying System –defence-blog.com Available athttps://defence-blog.com/ukrainian-army-uses-drones-as-remote-mine-laying-system/ [accessed 23 July, 2024] - Mankins, J. C. (1995). Technology readiness levels. White Paper, April 6(1995), p. 1995. Available:
https://spacese.spacegrant.org/SEModules/Technology%20Mods/Mankins_trl.pdf [Accessed 20.07.2025]. - Mansikka, H., Virtanen, K., & Harris, D. (2023). What we got here, is a failure to coordinate: Implicit and explicit coordination in air combat. Journal of Cognitive Engineering and Decision Making, 17, pp. 279–293. doi: 10.1177/15553434231179566
- Mizokami, K. (2018). Russia’s Tank Drone Performed Poorly in Syria –
popularmechanics.com . Available athttps://www.popularmechanics.com/military/weapons/a21602657/russias-tank-drone-performed-poorly-in-syria/ [accessed 23 July, 2024]. - Munir, A., Aved, A., & Blasch, E. (2022). Situational awareness: Techniques, challenges, and prospects. AI, 3(1), pp. 55–77. doi: 10.3390/ai3010005
- Naik, B. T., & Hashmi, M. F. (2023). YOLOv3-SORT: Detection and tracking player/ball in soccer sport. Journal of Electronic Imaging, 32(1), pp. 011003–011003. doi: 10.1117/1.JEI.32.1.011003
- Nguyen, P. T., Westerlund, T., & Peña Queralta, J. (2023). Vision-based safe autonomous UAV docking with panoramic sensors. Frontiers in Robotics and AI, 10, p. 1223157. doi: 10.3389/frobt.2023.1223157
- OpenCV Tracker API. (2023). Available at
https://docs.opencv.org/4.7.0/dc/d6b/group__tracking__legacy.html [accessed 8 June, 2023]. - Picam360 Developer Community, P. I. (2023). PICAM360. Available at
https://store.picam360.com/store#!/PICAM360-4KHDR/p/144496447/category=0 [accessed 8 June, 2023]. - Premachandra, C., Ueda, S., & Suzuki, Y. (2020). Detection and tracking of moving objects at road intersections using a 360-degree camera for driver assistance and automated driving. 8, pp. 135652–135660. doi: 10.1109/ACCESS.2020.3011430
- Qiu, Z., Wang, S., Zeng, Z., & Yu, D. (2019). Automatic visual defects inspection of wind turbine blades via YOLO-based small object detection approach. Journal of Electronic Imaging, 28(4), pp. 043023–043023. doi: 10.1117/1.JEI.28.4.043023
- Rankin, D. (2020). Unwrap. Available at
https://github.com/rankinstudio/unWrap [accessed 7 March, 2025]. - Rashed, H., Mohamed, E., Sistu, G., Kumar, V. R., Eising, C., & El-Sallab, A., et al. (2021). Generalized object detection on fisheye cameras for autonomous driving: Dataset, representations and baseline. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. IEEE, pp. 2272–2280.
- Ren, S., He, K., Girshick, R. B., & Sun, J. (2016). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, IEEE. doi: 10.1109/TPAMI.2016.2577031.
- Scharre, P. D. (2015). The opportunity and challenge of autonomous systems. Autonomous Systems: Issues for Defence Policymakers. HQ Sact, pp. 3–26.
- Schueler, K., Weiherer, T., Bouzouraa, E., & Hofmann, U. (2012). 360 degree multi sensor fusion for static and dynamic obstacles. In: 2012 IEEE Intelligent Vehicles Symposium. IEEE, pp. 692–697.
- Sejal, A. (2022). Unmanned Ground Vehicle Market Size, Share, Analysis, Trends –
alliedmarketresearch.com . Available athttps://www.alliedmarketresearch.com/unmanned-ground-vehicle-UGV-market [accessed 23 July, 2024]. -
Shmigel, P. (2023). Ukraine to Deploy Robots against Russian Troops –
kyivpost.com . Available athttps://www.kyivpost.com/post/20181 [accessed 23 July, 2024].ShmigelP. 2023 Ukraine to Deploy Robots against Russian Troops –kyivpost.com Available athttps://www.kyivpost.com/post/20181 [accessed 23 July, 2024] - Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), pp. 1–48. doi: 10.1186/s40537-019-0197-0
- Sutton, H. I. (2023). World’s First Specialized Explosive Naval Drone Unit Formed in Ukraine – Naval News –
navalnews.com . Available athttps://www.navalnews.com/naval-news/2023/08/worlds-first-specialized-explosive-naval-drone-unit-formed-in-ukraine/ [accessed 22 July, 2024]. - Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, PMLR. doi: 10.48550/arXiv.1905.11946.
- Tan, M., Pang, R., & Le, Q. V. (2019). EfficientDet: Scalable and efficient object detection. In: Conference on Neural Information Processing Systems.
- Vijayakumar, A., & Vairavasundaram, S. (2024). YOLO-based object detection models: A review and its applications. Multimedia Tools and Applications, 83(35), pp. 83535–83574. doi: 10.1007/s11042-024-18872-y
- Wang, K. H., & Lai, S. H. (2019). Object detection in curved space for 360-degree camera. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. doi: 10.1109/ICASSP.2019.8683093, pp. 3642–3646.
- Wolf, M. T., Assad, C., Kuwata, Y., Howard, A., Aghazarian, H., Zhu, D., et al. (2010). 360-degree visual detection and target tracking on an autonomous surface vehicle. Journal of Field Robotics, 27(6), pp. 819–833. doi: 10.1002/rob.20371
- Xu, Y., Fu, M., Wang, Q., Wang, Y., Chen, K., Xia, G.-S., et al. (2020). Gliding vertex on the horizontal bounding box for multi-oriented object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(4), pp. 1452–1459. doi: 10.1109/TPAMI.2020.2974745
- Yang, W., Qian, Y., Kämäräinen, J.-K., Cricri, F., & Fan, L. (2018). Object detection in equirectangular panorama. In: 2018 24th International Conference on Pattern Recognition (ICPR), IEEE. doi: 10.48550/arXiv.1805.08009, pp. 2190–2195.
- Yang, Y., Tang, D., Wang, D., Song, W., Wang, J., & Fu, M. (2020). Multi-camera visual SLAM for off-road navigation. Robotics and Autonomous Systems, 128, p. 103505. doi: 10.1016/j.robot.2020.103505
- Yeong, D. J., Velasco-Hernandez, G., Barry, J., & Walsh, J. (2021). Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors, 21(6), p. 2140. doi: 10.3390/s21062140
- Zelioli, L. (2024). Leveraging machine learning for maritime object detection and peatland classification, Doctoral dissertation. University of Turku.
- Zhang, J. M., Harman, M., Ma, L., & Liu, Y. (2022). Machine learning testing: Survey, landscapes and horizons. IEEE Transactions on Software Engineering, 48(1), pp. 1–36. doi: 10.1109/TSE.2019.2962027
- Zhang, X., Dong, X., Wei, Q., & Zhou, K. (2019). Real-time object detection algorithm based on improved YOLOv3. Journal of Electronic Imaging, 28(5), pp. 053022–053022. doi: 10.1117/1.JEI.28.5.053022
- Zhang, Y., Xiao, X., & Yang, X. (2017). Real-time object detection for 360-degree panoramic image using cnn. In: 2017 International Conference on Virtual Reality and Visualization (ICVRV). IEEE, pp. 18–23.
Language: English
Submitted on: Apr 30, 2025
Accepted on: Aug 24, 2025
Published on: Oct 15, 2025
Published by: National Defense University
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
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© 2025 Adrian Borzyszkowski, Christian Andersson, Luca Zelioli, Paavo Nevalainen, Jukka Heikkonen, published by National Defense University
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