📝 SummarXiv

Abstract

Automated inspection of transmission lines using UAVs is hindered by the difficulty of detecting small and ambiguous defects against complex backgrounds. Conventional detectors often suffer from detail loss due to strided downsampling, weak boundary sensitivity in lightweight backbones, and insufficient integration of global context with local cues. To address these challenges, we propose TinyDef-DETR, a DETR-based framework designed for small-defect detection. The method introduces a stride-free space-to-depth module for lossless downsampling, an edge-enhanced convolution for boundary-aware feature extraction, a cross-stage dual-domain multi-scale attention module to jointly capture global and local information, and a Focaler-Wise-SIoU regression loss to improve localization of small objects. Experiments conducted on the CSG-ADCD dataset demonstrate that TinyDef-DETR achieves substantial improvements in both precision and recall compared to competitive baselines, with particularly notable gains on small-object subsets, while incurring only modest computational overhead. Further validation on the VisDrone benchmark confirms the generalization capability of the proposed approach. Overall, the results indicate that integrating detail-preserving downsampling, edge-sensitive representations, dual-domain attention, and difficulty-adaptive regression provides a practical and efficient solution for UAV-based small-defect inspection in power grids.