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Ensuring reliable inspection of high-voltage transmission lines remains vital to maintaining smart grids. Nevertheless, human-based inspections are time-consuming, expensive, and dangerous, and even modern detection models frequently fail to detect small insulator flaws concealed within complex outdoor environments. This study proposes YOLOv11-RAH, a compact network architecture designed specifically for edge devices in smart inspection setups. It integrates the YOLOv11 backbone with a recurrent attention head that progressively enhances multi-scale feature representations via depthwise gated convolutions and terminates refinement upon reaching convergence, thereby boosting localization accuracy with almost no increase in inference time. Robustness to changes in perspective, lighting conditions, and object scale is further strengthened by a four-phase data augmentation protocol that includes geometric distortions, photometric adjustments, Mosaic blending, and random cropping. Evaluation was conducted on a merged insulator dataset compiled from two refined public benchmarks plus a fresh private UAV-collected corpus, capturing varied real-world outdoor scenarios and defect patterns. Broad comparative experiments against alternative detection approaches reveal steady performance gains from the proposed architecture. Detailed ablation analyses further verify the value of the novel attention mechanism and the complete augmentation strategy. In particular, YOLOv11-RAH obtains an approximate 5 percentage point gain in mAP compared with the standard YOLOv11n model on the aggregated public sets PTL-AI Furnas and CPLID, as well as on the private UAV collection AIDD, illustrating excellent cross-source generalization. Overall, these outcomes demonstrate that recurrent attention paired with purpose-built augmentation constitutes a practical and robust strategy for fully automated detection of insulator defects in intelligent power-line monitoring systems.