Glass insulators are critical components in transmission lines, and their defects can significantly impact the reliability of power systems. This paper proposes an improved defect detection algorithm for lightweight glass insulators based on enhanced RT-DETR, addressing issues related to low contrast and multi-scale defects. First, we introduce the lightweight backbone network RE-FasterNet, which enhances feature extraction efficiency and improves the detection of small targets and low-contrast defects through innovative partial duplication and an efficient multi-scale attention mechanism. Second, during the feature fusion stage, a partially repeated cross-stage feature fusion module is proposed to further enhance the detection capability for multi-scale defects. Finally, an attention scale sequence fusion framework is integrated into the small target detection head, significantly improving the network′s spatial feature extraction ability for small defects. Experimental results demonstrate that the proposed algorithm increases the mean average precision by 2.8%, reduces the model size by 23.6%, and decreases computational requirements by 13.1% compared to the benchmark model. In the domain of automatic defect detection for glass insulators, this approach exhibits strong practicality and broad applicability.