A novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images

2026-05-19

AI Summary

This study addresses the challenges of insulator defect detection in drone-captured images—namely, small defect scales, diverse failure patterns, complex backgrounds, and variable imaging conditions—by proposing a high-resolution YOLOv8 architecture augmented with a sparse Mixture-of-Experts (MoE) module. To the best of our knowledge, this is the first work to integrate the MoE mechanism into the YOLO family for this specific task. Furthermore, an LLM-based intelligent agent enhanced with tool-augmented capabilities is designed to autonomously orchestrate hyperparameter optimization and training procedures. The proposed method achieves state-of-the-art performance on insulator fault detection, attaining 0.9900 mAP@0.5 and 0.9515 mAP@0.5:0.95, significantly outperforming existing YOLO variants while striking an effective balance between detection accuracy and computational efficiency.

Abstract

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.

Problem

• insulator fault detection
• UAV images
• small defect regions
• heterogeneous fault patterns
• complex backgrounds

Innovation

• YOLO26-MoE
• Mixture-of-Experts
• LLM agent
• insulator fault detection
• UAV imagery