Hierarchical Deep Learning Model for Identifying Similar Targets in UAV Imagery

Accurate object detection in UAV imagery is critical for situational awareness, yet conventional deep learning models often struggle to distinguish between visually similar targets. To address this challenge, this study introduces a hierarchical deep learning architecture that decomposes the multi-class detection task into a structured, multi-level classification cascade. Our approach combines a high-recall Faster R-CNN for initial object proposal, specialized YOLO models for granular feature extraction, and a dedicated FT-Transformer for fine-grained classification. Experimental evaluation on a complex dataset demonstrated the effectiveness of this strategy. The hierarchical model achieved an aggregate F1-score of 93.9%, representing a 1.41% improvement over the 92.46% F1-score from a traditional, non-hierarchical baseline model. These results indicate that a modular, coarse-to-fine cascade can effectively reduce inter-class ambiguity, offering a scalable approach to improving object recognition in complex UAV-based monitoring environments. This work contributes a promising approach to developing more accurate and reliable situational awareness systems.