针对无人机巡检中缺陷小且模糊的问题，提出高效精准的检测框架。
TinyDef-DETR: A Transformer-Based Framework for Defect Detection in Transmission Lines from UAV Imagery
- 采用边缘增强残差网络与无步长下采样，保留细节特征。
- 多尺度注意力机制融合全局上下文与局部线索，提升小目标识别率。
- 适合电力巡检中对微小缺陷高精度检测的实时场景应用。
由于缺陷尺寸小、特征模糊且背景复杂，从无人机拍摄的输电线路图像中自动检测缺陷是一项挑战。本文提出基于DETR架构的TinyDef-DETR框架，实现高精度、高效率的输电线路缺陷检测。该模型包含四个核心组件：边缘增强的ResNet主干网络以强化边界敏感表征；无步长空间到深度模块实现保细节下采样；跨阶段双域多尺度注意力机制联合建模全局上下文与局部线索；以及聚焦式加权SIoU回归损失，提升小目标与困难样本的定位能力。在公开及真实数据集上的大量实验表明，TinyDef-DETR在保持较低计算开销的同时，实现了优异的检测性能与强泛化能力。其准确率与效率使其成为无人机输电线路缺陷检测的理想选择，尤其适用于小而模糊目标的检测场景。
原文摘要 · Abstract (English)
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.
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