A Decoupled Hierarchical Fault Detection Method for Insulator Parallel Gaps: Integrating Lightweight Localization and Attention-Based Diagnosis

Insulator parallel gaps, as critical lightning protection components in high-voltage transmission lines, are prone to faults, such as short circuits and excessive spacing, which compromise line safety and power supply reliability. However, detecting insulator parallel gaps from unmanned aerial vehicle (UAV) captured images is challenged by complex backgrounds, structural distortion, and occlusion. Thus, a hierarchical detection method incorporating lightweight localization and attention mechanism-based diagnosis (KLSD-HDet) is proposed, which uses a localization network to capture the faults and then conducts fault diagnosis. First, to accurately capture target objects, a lightweight fault localization network (KDeFus-LNet) is designed, with nonlinear feature extraction (NFE) and target localization capabilities in complex backgrounds enhanced. Second, leveraging the spatial geometric information from 3-D fault models, a multiview fault image generation method is developed to compensate for the missing feature representations of partial viewpoints in real-world datasets. Then, a cross-space learning and multiscale residual-based fault diagnosis network (S-CR2-DNet) is proposed to improve multiview fault representation understanding and diagnostic accuracy. Finally, knowledge distillation is employed to lightweight S-CR2-DNet, enhancing its practical applicability. Extensive experiments validate that KLSD-HDet outperforms the state-of-the-art (SOTA) methods, achieving 94.49% detection precision, improved by 6.65% compared to the SOTA algorithms.