DCAF-DETR: Content-Adaptive Resampling with Scale-Conditioned Selective Aggregation for fuzzy Small-Object Detection in Real-Time UAV Power-Line Inspection

Abstract Real-time detection of small, visually ambiguous targets in UAV power-line inspection presents a significant challenge due to the limited computational capacity of onboard processors. In this work, we propose DCAF-DETR, a lightweight detection framework designed to enhance the recognition of fuzzy small objects within the RT-DETR paradigm. The architecture integrates a CGRepBlock backbone and the DySample content-adaptive upsampling module to preserve fine visual details, alongside contextual modules to maintain semantic stability. To address feature aliasing, three scale-discriminative fusion components-Fusion-CADA, Fusion-DCPA, and Fusion-SDFM-are introduced. To optimize the accuracy-efficiency trade-off, DCAF-DETR prioritizes high inference speed and low resource consumption. Evaluated on public datasets, the model achieves 76.5\% $AP_{50}$ and 35.6\% $AP_{50:95}$. Although the high-precision metric is marginally lower than heavy state-of-the-art models (e.g., YOLOv8-X), our framework runs at 65 FPS with only 12.55M parameters and 40.3 GFLOPs-reducing computational cost by approximately 80\% compared to large-scale counterparts. This confirms that DCAF-DETR achieves a superior Pareto frontier suitable for resource-constrained UAV platforms. Validation on a custom dataset ($AP_{50}$ of 80.5\%) further confirms its robustness, demonstrating DCAF-DETR as a practical solution for automated inspection tasks.