Object prediction and detection of ground-based weapon with an improved YOLO11 approach: Focusing on assumptions underlying operational environments and UAV-captured features related to PLZ-05 Self-Propelled Howitzer

Hanyul Ryu; Mingyu Park; Dae-Yeol Kim

doi:10.37944/jams.v7i3.256

Authors

Hanyul Ryu School of ComputerScience and Engineering, Kyungnam Unviersity
Mingyu Park School of Computer Science and Engineering, Kyungnam University
Dae-Yeol Kim Kyungnam University, Department of Artificial Intelligence

DOI:

https://doi.org/10.37944/jams.v7i3.256

Keywords:

ground-based weapon systems, self-propelled Howitzer, trajectory prediction, YOLOv11, object detection

Abstract

The utilization of UAV-based detection technologies in ground weapon system analysis plays a crucial role in supporting real-time tactical decision-making. While previous studies have primarily focused on improving the detection and classification performance of military objects using UAVs, the current study proposes a novel system that not only detects military objects in simulated UAV operational environments but also analyzes the elevation and azimuth angles of detected gun barrels. For object detection, the YOLO11 model was employed in conjunction with the BCEF loss function to enhance detection performance. The proposed system was validated across various environments using synthetically generated images simulating complex battlefield conditions, including rain, challenging terrain, and low-light environments. Even under these adverse conditions, the model demonstrated high detection accuracy and reliability. This study highlights the potential of UAV-based object detection technology as a tactical decision-making support tool, extending its utility from reconnaissance and identification to broader operational roles. Future research need to further evaluate the performance of the proposed model with experimental validation in real-world UAV operational conditions, emphasizing real-time data collection and analysis frameworks.

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Author Biographies

Hanyul Ryu, School of ComputerScience and Engineering, Kyungnam Unviersity

(First author) Kyungnam University, School of Computer Science and Engineering, Undergraduate Student, [email protected], https://orcid.org/0009-0008-0891-6538

Mingyu Park, School of Computer Science and Engineering, Kyungnam University

(Co-author) Kyungnam University, School of Computer Science and Engineering, Undergraduate Student, [email protected], https://orcid.org/0009-0009-3490-5178

Dae-Yeol Kim, Kyungnam University, Department of Artificial Intelligence

(Corresponding author) Kyungnam University, Department of Artificial Intelligence, Professor, [email protected], https://orcid.org/0000-0003-3242-1902

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