RSDMF-DETR: A Remote Sensing Direction-aware Multi-scale Fusion Detection Transformer

Abstract With the expansion of wind power scale, UAV-based remote sensing technology has become an important approach for wind turbine object detection in intelligent inspection, operation and maintenance, and wind energy resource assessment. Compared with traditional manual inspections, UAV remote sensing provides advantages of non-contact operation, wide coverage, rapid acquisition, and high spatial resolution. However, wind turbine objects exhibit significant scale variations, diverse orientations, and complex backgrounds, posing challenges to feature representation, multi-scale fusion, and robust detection. To address these challenges, this paper constructs a UAV-based remote sensing dataset and proposes a Remote Sensing Direction-aware Multi-scale Fusion Detection Transformer (RSDMF-DETR), a task-oriented, stage-wise framework that integrates existing feature extraction components with two self-designed modules, Remote Spatial Direction Attention (RSDA) and Scale-Enhanced Bidirectional Channel-Spatial Attention (SE-BCSA). Specifically, the backbone adopts the existing Context-Guided Block (CGB) to jointly model local texture information and contextual semantic cues, thereby improving feature representation under complex backgrounds. The Remote Direction-Aware Encoding Network (RDEN) integrates existing GSConv and VoVGSCSP components with the proposed RSDA module to enhance feature encoding, cross-layer aggregation, and directional structural representation. Finally, the Remote Multi-scale Adaptive Fusion (RMAF) stage combines the existing DynamicScalSeq component with the proposed SE-BCSA module to enhance cross-scale feature alignment and channel-spatial feature refinement. Experimental results on the self-built UAV remote sensing dataset demonstrate that RSDMF-DETR achieves 89.5% 〖AP〗_(IoU=50) and 67.1% 〖AP〗_(IoU=50:95), representing improvements of 2.0 and 0.8 percentage points over RT-DETR-R18, respectively. These results demonstrate that the proposed stage-wise strategy effectively integrates contextual, directional, and multi-scale feature representations for wind turbine detection under complex remote sensing conditions. RSDMF-DETR may provide target-level information for wind farm inspection, localization, and spatial information management. Its deployment and performance on actual UAV or edge-computing platforms have not yet been experimentally validated and remain topics for future work.