Boosting YOLO11: Global Attention & Hyperparameter Tuning  for High-Fidelity Military Aircraft Detection

Satyo Widijanuarto; Ema Utami

doi:10.29207/resti.v9i6.7102

Authors

Satyo Widijanuarto Universitas Amikom Yogyakarta
Ema Utami Universitas Amikom Yogyakarta

DOI:

https://doi.org/10.29207/resti.v9i6.7102

Keywords:

hyperparameters optimization, Global Attention Mechanism, object detection, military aircraft, YOLO11

Abstract

Military aircraft detection from aerial and satellite imagery is crucial for strategic surveillance and intelligence. This study evaluated the impact of the Global Attention Mechanism (GAM) and hyperparameter optimization on the performance of the YOLO11 model for military aircraft detection. Utilizing a traditional YOLO model as a baseline, we compared precision, recall, and mean Average Precision (mAP) metrics across various configurations. These configurations included the implementation of GAM and variations in n, s variant of YOLO11, optimizers (Adam, NAdam, RAdam, Adamax, AdamW, SGD) and learning rates (0.01, 0.001, 0.0001, 0.00417). Experimental results demonstrate that the integration of GAM significantly enhances the model's detection capabilities within 300 iterations, particularly when combined with the Adamax optimizer and a learning rate of 0.001. This specific configuration achieved the highest mAP performance of 98.5%, outperforming other setups. Further confusion matrix analysis confirmed high accuracy in classifying various aircraft types, while also highlighting some challenges in distinguishing certain classes. The primary contribution of this study is the empirical demonstration of improved military aircraft detection performance by YOLO11 through the utilization of four global attention mechanism modules and effective hyperparameter tuning. These findings offer valuable insights for developing more accurate and robust object detection systems for defense and security applications.

Downloads

Download data is not yet available.

References

K. Liu, Z. Xu, Y. Liu, and G. Xu, “Military Aircraft Recognition Method Based on Attention Mechanism in Remote Sensing Images,” IET Image Process., vol. 19, no. 1, p. e70069, Jan. 2025, doi: https://doi.org/10.1049/ipr2.70069.

I. Bayraktar and M. Bakirci, “Attention-Augmented YOLO11 for High-Precision Aircraft Detection in Synthetic Aperture Radar Imagery,” in 2025 27th International Conference on Digital Signal Processing and its Applications (DSPA), 2025, pp. 1–6. doi: 10.1109/DSPA64310.2025.10977903.

X. Chen et al., “DET-YOLO: An Innovative High-Performance Model for Detecting Military Aircraft in Remote Sensing Images,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 17, pp. 17753–17771, 2024, doi: 10.1109/JSTARS.2024.3462745.

F. N. M. Zamri, T. S. Gunawan, S. H. Yusoff, A. A. Alzahrani, A. Bramantoro, and M. Kartiwi, “Enhanced Small Drone Detection Using Optimized YOLOv8 With Attention Mechanisms,” IEEE Access, vol. 12, pp. 90629–90643, 2024, doi: 10.1109/ACCESS.2024.3420730.

B. Huang, Y. Ding, G. Liu, G. Tian, and S. Wang, “ASD-YOLO: An aircraft surface defects detection method using deformable convolution and attention mechanism,” Measurement, vol. 238, p. 115300, 2024, doi: https://doi.org/10.1016/j.measurement.2024.115300.

W. Yu et al., “MAR20： A benchmark for military aircraft recognition in remote sensing images,” Natl. Remote Sens. Bull., 2023, doi: 10.11834/jrs.20222139.

A. Rácz, D. Bajusz, and K. Héberger, “Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification,” Molecules, vol. 26, 2021, doi: 10.3390/molecules26041111.

K. Dobbin and R. M. Simon, “Optimally splitting cases for training and testing high dimensional classifiers,” 2011. doi: 10.1186/1755-8794-4-31.

R. Khanam and M. Hussain, “YOLOv11: An Overview of the Key Architectural Enhancements,” ArXiv, vol. abs/2410.1, 2024, [Online]. Available: http://arxiv.org/abs/2410.17725

Y. Liu, Z. Shao, and N. Hoffmann, “Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions,” 2021. [Online]. Available: http://arxiv.org/abs/2112.05561

F. Wang, H. Wang, Z. Qin, and J. Tang, “UAV target detection algorithm based on improved YOLOv8,” IEEE Access, vol. PP, p. 1, Jan. 2023, doi: 10.1109/ACCESS.2023.3325677.

D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., Dec. 2015, [Online]. Available: https://arxiv.org/pdf/1412.6980

E. Sudewo, M. Biddinika, R. Umar, and A. Fadlil, “Evaluating the Impact of Optimizer Hyperparameters on ResNet in Hanacaraka Character Recognition,” Preserv. Digit. Technol. Cult., Feb. 2025, doi: 10.1515/pdtc-2024-0061.

L. Liu et al., On the Variance of the Adaptive Learning Rate and Beyond. 2019. doi: 10.48550/arXiv.1908.03265.

L. Bottou, “Large-Scale Machine Learning with Stochastic Gradient Descent,” Proc. of COMPSTAT, Sep. 2010, doi: 10.1007/978-3-7908-2604-3_16.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-Based Convolutional Networks for Accurate Object Detection and Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 1, pp. 142–158, 2016, doi: 10.1109/TPAMI.2015.2437384.

L. Peng, H. Wang, and J. Li, “Uncertainty Evaluation of Object Detection Algorithms for Autonomous Vehicles,” Automot. Innov., vol. 4, no. 3, pp. 241–252, 2021, doi: 10.1007/s42154-021-00154-0.

L. Shen, H. Tao, Y. Ni, Y. Wang, and V. Stojanovic, “Improved YOLOv3 model with feature map cropping for multi-scale road object detection,” Meas. Sci. Technol., vol. 34, no. 4, p. 45406, 2023, doi: 10.1088/1361-6501/acb075.

X. Zhang et al., “Automatic learning for object detection,” Neurocomputing, vol. 484, pp. 260–272, 2022, doi: https://doi.org/10.1016/j.neucom.2022.02.012.

C.-T. Hu et al., “HARNESSING YOLO ALGORITHMS FOR EFFICIENT BREAST CANCER DETECTION IN MAMMOGRAPHY,” J. Med. Imaging Radiat. Sci., vol. 54, no. 3, p. S7, Sep. 2023, doi: 10.1016/j.jmir.2023.06.025.

C. Meng, S. Liu, and Y. Yang, “DMAC-YOLO: A High-Precision YOLO v5s Object Detection Model with a Novel Optimizer,” in 2024 International Joint Conference on Neural Networks (IJCNN), 2024, pp. 1–8. doi: 10.1109/IJCNN60899.2024.10650645.

M. Zhang and L. Yin, “Solar Cell Surface Defect Detection Based on Improved YOLO v5,” IEEE Access, vol. 10, pp. 80804–80815, 2022, doi: 10.1109/ACCESS.2022.3195901.

X. Zhao et al., “G-YOLO: A Lightweight Infrared Aerial Remote Sensing Target Detection Model for UAVs Based on YOLOv8,” 2024. doi: 10.3390/drones8090495.

Z. Chen, L. Cao, and Q. Wang, “YOLOv5-Based Vehicle Detection Method for High-Resolution UAV Images,” Mob. Inf. Syst., vol. 2022, no. 1, p. 1828848, Jan. 2022, doi: https://doi.org/10.1155/2022/1828848.

I. Amelia Dewi and M. A. Negara Ekha Salawangi, “High performance of optimizers in deep learning for cloth patterns detection,” IAES Int. J. Artif. Intell. (IJ-AI); Vol 12, No 3 Sept. 2023, 2023, doi: 10.11591/ijai.v12.i3.pp1407-1418.

W. Cheng, R. Pu, and B. Wang, “AMC: Adaptive Learning Rate Adjustment Based on Model Complexity,” 2025. doi: 10.3390/math13040650.

N. Jegham, C. Y. Koh, M. Abdelatti, and A. Hendawi, YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLO11, and Their Previous Versions. 2025. doi: 10.13140/RG.2.2.15952.83201.

W. Zhu, X. Han, K. Zhang, S. Lin, and J. Jin, “Application of YOLO11 Model with Spatial Pyramid Dilation Convolution (SPD-Conv) and Effective Squeeze-Excitation (EffectiveSE) Fusion in Rail Track Defect Detection.,” Sensors (Basel)., vol. 25, no. 8, Apr. 2025, doi: 10.3390/s25082371.

A. M. Moraes, L. F. Pugliese, R. F. Santos, G. B. Vitor, R. A. Braga, and F. R. Silva, “Effectiveness of YOLO Architectures in Tree Detection: Impact of Hyperparameter Tuning and SGD, Adam, and AdamW Optimizers,” 2025. doi: 10.3390/standards5010009.