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

• URL: http://arxiv.org/abs/2509.06035v8
• Date: Mon, 03 Nov 2025 15:03:19 GMT
• Title: TinyDef-DETR: A Transformer-Based Framework for Defect Detection in Transmission Lines from UAV Imagery
• Authors: Feng Shen, Jiaming Cui, Wenqiang Li, Shuai Zhou,
• Abstract summary: TinyDef-DETR is a framework designed to achieve accurate and efficient detection of transmission line defects from UAV-acquired images.<n>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, and a Focaler-Wise-SIoU regression loss to improve the localization of small and difficult objects.
• Score: 12.48571944931548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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