@article{ART003202471},
author={Kim, Junsub and Choi, Dongnyeok},
title={Comparative analysis on few-shot models performance for improving object detection in the military Domain},
journal={Journal of Advances in Military Studies},
issn={2635-5531},
year={2025},
volume={8},
number={1},
pages={1-13},
doi={10.37944/jams.v8i1.277}
TY - JOUR
AU - Kim, Junsub
AU - Choi, Dongnyeok
TI - Comparative analysis on few-shot models performance for improving object detection in the military Domain
JO - Journal of Advances in Military Studies
PY - 2025
VL - 8
IS - 1
PB - Institute of Defense Acquisition Program
SP - 1
EP - 13
SN - 2635-5531
AB - The application of Object Detection (OD) techniques in the military and defense domain is often restricted by stringent security requirements and limited data availability. To overcome these challenges, the present study investigates the potential of Few-Shot Object Detection (FSOD) for military applications. A military vehicle image dataset, composed of real-world defense imagery, was constructed for this purpose. Four representative object detection models—YOLO, DETR, GLIP, and CD-ViTO—were fine-tuned under 1-shot, 5-shot, and 10-shot conditions. The model performance was evaluated using mean Average Precision(mAP). Notably, the CD-ViTO model's cross-domain generalization capability was further examined by comparing its performance on this military dataset against public benchmarks previously used in FSOD studies. Experimental results demonstrate that CD-ViTO achieved superior mAP scores, highlighting the viability of FSOD for efficient and accurate object detection in military and defense applications.
KW - object detection;military domain;images of military vehicles;few-shot learning;model performance evaluation
DO - 10.37944/jams.v8i1.277
ER -
Kim, Junsub and Choi, Dongnyeok. (2025). Comparative analysis on few-shot models performance for improving object detection in the military Domain. Journal of Advances in Military Studies, 8(1), 1-13.
Kim, Junsub and Choi, Dongnyeok. 2025, "Comparative analysis on few-shot models performance for improving object detection in the military Domain", Journal of Advances in Military Studies, vol.8, no.1 pp.1-13. Available from: doi:10.37944/jams.v8i1.277
Kim, Junsub, Choi, Dongnyeok "Comparative analysis on few-shot models performance for improving object detection in the military Domain" Journal of Advances in Military Studies 8.1 pp.1-13 (2025) : 1.
Kim, Junsub, Choi, Dongnyeok. Comparative analysis on few-shot models performance for improving object detection in the military Domain. 2025; 8(1), 1-13. Available from: doi:10.37944/jams.v8i1.277
Kim, Junsub and Choi, Dongnyeok. "Comparative analysis on few-shot models performance for improving object detection in the military Domain" Journal of Advances in Military Studies 8, no.1 (2025) : 1-13.doi: 10.37944/jams.v8i1.277
Kim, Junsub; Choi, Dongnyeok. Comparative analysis on few-shot models performance for improving object detection in the military Domain. Journal of Advances in Military Studies, 8(1), 1-13. doi: 10.37944/jams.v8i1.277
Kim, Junsub; Choi, Dongnyeok. Comparative analysis on few-shot models performance for improving object detection in the military Domain. Journal of Advances in Military Studies. 2025; 8(1) 1-13. doi: 10.37944/jams.v8i1.277
Kim, Junsub, Choi, Dongnyeok. Comparative analysis on few-shot models performance for improving object detection in the military Domain. 2025; 8(1), 1-13. Available from: doi:10.37944/jams.v8i1.277
Kim, Junsub and Choi, Dongnyeok. "Comparative analysis on few-shot models performance for improving object detection in the military Domain" Journal of Advances in Military Studies 8, no.1 (2025) : 1-13.doi: 10.37944/jams.v8i1.277
