YOLOv11-RAH: A recurrent attention-enhanced edge intelligence network for UAV-based power transmission line insulator inspection

Abstract

Reliable inspection of power transmission lines is essential for smart grid maintenance, yet manual surveys are slow, costly, and risky, and current detectors still struggle with small insulator defects hidden in complex outdoor scenes. This paper introduces YOLOv11-RAH, a lightweight network framework designed for edge deployment in intelligent inspection systems that pairs the YOLOv11 backbone with a recurrent attention head. The head iteratively refines multi-scale features through depthwise gated convolutions and halts once convergence is reached, strengthening localization without noticeable latency. A four stage augmentation scheme, which are geometric, photometric, Mosaic, and random crop transformations, further improves robustness to viewpoint, illumination, and scale variations. For evaluation, a consolidated insulator corpus is assembled from two cleaned public benchmarks and a newly private UAV dataset, covering diverse outdoor conditions and defect categories. Extensive experiments with other detection algorithms show the consistent accuracy gains of the proposed network improvement, and ablation studies further confirm the benefits of the proposed attention module and augmentation pipeline. In particular, YOLOv11-RAH achieves roughly 5 percentage mAP improvement over YOLOv11n base model on both the consolidated public datasets PTL-AI Furnas and CPLID, and the private UAV dataset AIDD, underscoring its transferability across data sources. These findings suggest that iterative attention combined with targeted data augmentation offers an efficient and reliable solution for automatic insulator condition detection in intelligent inspection networks.