Object prediction and detection of ground-based weapon with an improved YOLO11 approach
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 cur...
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| Format: | Article |
| Language: | English |
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Institute of Defense Acquisition Program
2024-12-01
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| Series: | 선진국방연구 |
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| Online Access: | https://journal.idap.re.kr/index.php/JAMS/article/view/256 |
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| _version_ | 1841557362560729088 |
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| author | Hanyul Ryu Mingyu Park Dae-Yeol Kim |
| author_facet | Hanyul Ryu Mingyu Park Dae-Yeol Kim |
| author_sort | Hanyul Ryu |
| collection | DOAJ |
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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.
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| format | Article |
| id | doaj-art-838e86ab302546dfa4db3b12571dd1d0 |
| institution | Kabale University |
| issn | 2635-5531 2636-1329 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Institute of Defense Acquisition Program |
| record_format | Article |
| series | 선진국방연구 |
| spelling | doaj-art-838e86ab302546dfa4db3b12571dd1d02025-01-06T11:39:41ZengInstitute of Defense Acquisition Program선진국방연구2635-55312636-13292024-12-0173Object prediction and detection of ground-based weapon with an improved YOLO11 approachHanyul Ryu0Mingyu Park1Dae-Yeol Kim2School of ComputerScience and Engineering, Kyungnam UnviersitySchool of Computer Science and Engineering, Kyungnam UniversityKyungnam University, Department of Artificial Intelligence 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. https://journal.idap.re.kr/index.php/JAMS/article/view/256ground-based weapon systemsself-propelled Howitzertrajectory predictionYOLOv11object detection |
| spellingShingle | Hanyul Ryu Mingyu Park Dae-Yeol Kim Object prediction and detection of ground-based weapon with an improved YOLO11 approach 선진국방연구 ground-based weapon systems self-propelled Howitzer trajectory prediction YOLOv11 object detection |
| title | Object prediction and detection of ground-based weapon with an improved YOLO11 approach |
| title_full | Object prediction and detection of ground-based weapon with an improved YOLO11 approach |
| title_fullStr | Object prediction and detection of ground-based weapon with an improved YOLO11 approach |
| title_full_unstemmed | Object prediction and detection of ground-based weapon with an improved YOLO11 approach |
| title_short | Object prediction and detection of ground-based weapon with an improved YOLO11 approach |
| title_sort | object prediction and detection of ground based weapon with an improved yolo11 approach |
| topic | ground-based weapon systems self-propelled Howitzer trajectory prediction YOLOv11 object detection |
| url | https://journal.idap.re.kr/index.php/JAMS/article/view/256 |
| work_keys_str_mv | AT hanyulryu objectpredictionanddetectionofgroundbasedweaponwithanimprovedyolo11approach AT mingyupark objectpredictionanddetectionofgroundbasedweaponwithanimprovedyolo11approach AT daeyeolkim objectpredictionanddetectionofgroundbasedweaponwithanimprovedyolo11approach |