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.