Insulator-DETR: A Detection Transformer Tailored for Insulator Defect Inspection Based on UAV Remote Sensing

Reliable inspection of insulator defects from unmanned aerial vehicle (UAV) remote sensing imagery is essential for the safe operation of power transmission systems. However, the task remains challenging due to fine-grained defect patterns, thin structures, large-scale variations, and complex backgrounds in aerial scenes. To address these issues, we propose Insulator-DETR, an end-to-end detection transformer specifically designed for UAV-based insulator defect inspection. The proposed framework preserves the set-prediction paradigm of DETR while introducing task-oriented modifications. In the encoder, a Parallel Attention MLP integrates multi-scale spatial perception and phase-aware token interaction to enhance the representation of fine textures and structural details. In the decoder, a Masked Linear Attention mechanism combines efficient global modeling with local contextual aggregation, enabling accurate localization of irregular defects. Furthermore, a convolutional feed-forward design is adopted to strengthen spatial interactions. Extensive experiments on two newly annotated UAV insulator-defect datasets and a public benchmarks demonstrate that Insulator-DETR consistently outperforms state-of-the-art detectors in both detection accuracy and recall.