Hierarchical Deep Learning Model for Identifying Similar Targets in UAV Imagery

Abstract

Highlights: What are the main findings? A hierarchical deep learning model achieves a 94.9% F1-score in UAV object detection, outperforming the non-hierarchical baseline by 2.44%. The proposed coarse-to-fine cascade architecture effectively resolves inter-class ambiguity by systematically refining classification through specialized deep learning stages. What are the implications of the main findings? The proposed architecture enables the development of more accurate and reliable situational awareness systems for UAVs, reducing critical errors in target identification. This work provides a scalable and robust solution for complex computer vision tasks, demonstrating the superiority of modular, specialized models over monolithic approaches in UAV applications. 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 (Formula presented.) -score of 93.9%, representing a 1.41% improvement over the 92.46% (Formula presented.) -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.