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

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.