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arXiv · 2509.06035

TinyDef-DETR: A Transformer-Based Framework for Defect Detection in Transmission Lines from UAV Imagery

Feng Shen·Jiaming Cui·Wenqiang Li·Shuai Zhou
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Abstract

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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Feng Shen, Jiaming Cui, Wenqiang Li, Shuai Zhou. 2025-09-07. TinyDef-DETR: A Transformer-Based Framework for Defect Detection in Transmission Lines from UAV Imagery. https://arxiv.org/abs/2509.06035

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