FA-DETR: Frequency-Aware DETR Based Object Detection for Uncrewed Aerial Vehicle Images

Detection Transformer (DETR) has achieved remarkable performance across diverse object detection tasks. However, the long-range similarity computation inherent to its self-attention mechanism exhibits some limitations in detecting weak and small objects, particularly for structurally similar and densely distributed unmanned aerial vehicle (UAV) objects. To alleviate this limitation, this letter proposes a Frequency-Aware DETR (FA-DETR) that integrates frequency domain learning. FA-DETR incorporates three key modules into RT-DETR, including Adaptive Frequency Adjustment (AdaFA) module, Spatial-Frequency Feature Fusion (S3F) module, and Cross-Level Feature Compensation (CLFC) module. AdaFA splits input features into several frequency sub-bands and refines them through dynamic reconfiguration, which can enhance feature informativeness of weak and small objects. Through learnable parameters, S3F selectively combines spatial-frequency features across different levels and mitigates inherent feature biases. To further preserve semantic-structural consistency, CLFC employs a self-compensation approach that enriches high-level representations with structural details from low-level features using multiscale cross-attention. FA-DETR is built by plugging the AdaFA, S3F and CLFC modules into RT-DETR. Extensive experiments on benchmark datasets demonstrate that FA-DETR obtains state-of-the-art detection performance. Specifically, it provides 2.8% mAP improvements over RT-DETR on the VisDrone-2019 dataset.