A novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images
For power grid maintenance, this provides an incremental improvement in insulator fault detection accuracy using a hybrid architecture and LLM-based optimization.
The paper proposes YOLO26-MoE, integrating a sparse Mixture-of-Experts module into YOLO26 for insulator fault detection in UAV images, achieving 0.9900 mAP@0.5 and 0.9515 mAP@0.5:0.95, outperforming latest YOLO versions.
The inspection of electrical power line insulators is essential for ensuring grid reliability and preventing failures caused by damaged or degraded insulation components. In recent years, Unmanned Aerial Vehicles (UAVs) combined with deep learning-based vision systems have emerged as an effective solution for automating this process. However, insulator fault detection remains challenging due to small defect regions, heterogeneous fault patterns, complex backgrounds, and varying imaging conditions. To address these challenges, this paper proposes an optimized YOLO26-MoE, a novel object detection architecture that integrates a sparse Mixture-of-Experts (MoE) module into the high-resolution branch of the YOLO26 detector. The proposed modification enables adaptive feature refinement for subtle and diverse fault patterns while preserving the efficiency of a one-stage detection framework. Hyperparameter optimization, final training, and evaluation were coordinated through a tool-augmented Large Language Model (LLM) agent. The proposed model achieved 0.9900 mAP@0.5 and 0.9515 mAP@0.5:0.95, outperforming the latest YOLO versions. These results demonstrate that the proposed model provides an effective and reliable solution for UAV-based insulator fault detection.