ADR-YOLO: An Adaptive Dual-Refinement Network for Object Detection in Aerial Images

Aerial object detection is a fundamental component of UAV-based surveillance and Earth observation, supporting applications such as traffic monitoring, urban management, and emergency search-and-rescue. However, complex backgrounds and pronounced scale variation in aerial imagery pose challenges to feature representation learning across shallow feature extraction, deep semantic modeling and multi-scale fusion. To tackle these challenges, we propose an Adaptive Dual-Refinement YOLO (ADR-YOLO), featuring two stage-specific refinements in the backbone and a scale-aware fusion strategy in the neck. At shallow stages, a Denoising Module (DNM) suppresses background-induced activations while preserving fine-grained structural cues through spatial structure modeling and element-wise modulation. In the deep backbone, a Gated Selective Attention (GSA) mechanism employs a contrast-enhanced attention formulation with semantic-adaptive gating to improve foreground discrimination under background interference. In the neck, a Channel-Compressed Fusion Module (CCFM) aligns multi-level features via bottleneck-style channel mapping, and leverages Global Response Normalization (GRN) to recalibrate channel responses and reduce fusion redundancy. Extensive experiments on VisDrone2019-DET, TinyPerson, and DIOR demonstrate that ADR-YOLO outperforms state-of-the-art detectors with marginal additional computational overhead. Our source code will be released on GitHub.