GURLKNet gated unified reparameterized large kernel network for insulator defect detection.

With the continuous advancement of unmanned aerial vehicles (UAVs) and computer vision technologies, UAV-based insulator defect detection has become a crucial approach to ensuring the safety of power systems. However, this task still faces multiple challenges, such as scale imbalance, blurred edges, and complex backgrounds. To address these issues, this paper proposes a Gated Unified Reparameterized Large Kernel Network (GURLKNet) to enhance insulator defect detection performance. Specifically, a Gated Unified Reparameterized Large Kernel Module (GUR-LKM) is designed to suppress redundant channels through a gating mechanism and introduce partial depthwise convolution structures, which significantly expand the receptive field. Furthermore, an Edge-Guided Feature Stem (EGFStem) is constructed by integrating the Sobel edge operator with a texture-guided mechanism to strengthen shallow features' perception of structural boundaries. In addition, a Context-Interactive Fusion Network (CIFNet) is introduced, employing a multi-scale attention-guided strategy to alleviate semantic inconsistency and improve the semantic expression and localization accuracy of feature fusion. The experimental results on several insulator defect datasets show that the proposed method demonstrates strong overall accuracy while maintaining low computational cost, and outperforms mainstream object detection models on most evaluation metrics. Compared to the baseline model, GURLKNet achieves a mAP50 improvement of 3.5% on the Insulator-DET dataset and 0.9% on the IDID dataset. This study provides an efficient and reliable solution for intelligent insulator inspection, promoting the engineering application and deployment of object detection technology in low-altitude power system sensing.