Object prediction and detection of ground-based weapon with an improved YOLO11 approach

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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.