Automatic Target Recognition (ATR) from Unmanned Aerial Vehicle (UAV) images poses significant challenges due to complex landscapes, adverse weather conditions, and low-resolution imagery. This study proposes an enhanced YOLOv8-SR model that leverages optimized super-resolution techniques and hyperparameters to improve detection accuracy and robustness. The model integrates the Inception-NeXt block into the YOLOv8 architecture, enabling detailed feature extraction from UAV images. Pre-processing steps, including contrast enhancement, noise reduction, data augmentation, and normalization, further improve image quality and model performance. Experiments using the VisDrone dataset reveal that the YOLOv8-SR model, optimized with the SGD optimizer, achieves the highest mean Average Precision (mAP@50) of 49.761%, outperforming both Adam and AdamW optimizers. The proposed method significantly enhances target recognition capabilities in UAV images, demonstrating its potential for applications in surveillance, reconnaissance, and security.
Mishra et al. (Sun,) studied this question.