Automated defect detection from UAV imagery of transmission lines is a challenging task due to the small size, ambiguity, and complex backgrounds of defects. This paper proposes TinyDef-DETR, a DETR-based framework designed to achieve accurate and efficient detection of transmission line defects from UAV-acquired images. The model integrates four major components: an edge-enhanced ResNet backbone to strengthen boundary-sensitive representations, a stride-free space-to-depth module to enable detail-preserving downsampling, a cross-stage dual-domain multi-scale attention mechanism to jointly model global context and local cues, and a Focaler-Wise-SIoU regression loss to improve the localization of small and difficult objects. Together, these designs effectively mitigate the limitations of conventional detectors. Extensive experiments on both public and real-world datasets demonstrate that TinyDef-DETR achieves superior detection performance and strong generalization capability, while maintaining modest computational overhead. The accuracy and efficiency of TinyDef-DETR make it a suitable method for UAV-based transmission line defect detection, particularly in scenarios involving small and ambiguous objects.

翻译：从无人机影像中自动检测输电线路缺陷是一项具有挑战性的任务，这主要源于缺陷目标尺寸小、特征模糊以及背景复杂。本文提出TinyDef-DETR，一种基于DETR的框架，旨在从无人机获取的图像中实现准确且高效的输电线路缺陷检测。该模型集成了四个核心组件：用于增强边界敏感表征的边缘增强ResNet主干网络、实现细节保持下采样的无步长空间到深度模块、联合建模全局上下文与局部线索的跨阶段双域多尺度注意力机制，以及用于提升小目标和困难目标定位精度的Focaler-Wise-SIoU回归损失函数。这些设计共同有效缓解了传统检测器的局限性。在公开数据集和真实场景数据集上的大量实验表明，TinyDef-DETR在保持适中计算开销的同时，实现了优越的检测性能和强大的泛化能力。TinyDef-DETR的准确性与效率使其成为基于无人机的输电线路缺陷检测的适用方法，尤其适用于涉及小目标和模糊目标的场景。