Abstract
Efficient defect detection of insulators in transmission lines is critical to ensuring the reliability and safety of power systems. However, adverse outdoor conditions such as rain, fog, and low illumination can reduce the contrast between targets and background, resulting in blurred defect details. Meanwhile, insulators often coexist with towers, conductors, and other elements within complex backgrounds, which can lead to inaccurate target segmentation and subsequently cause missed or false detections. This study introduces a lightweight insulator defect detection algorithm, SML-YOLO, designed for harsh weather scenarios. Built upon the YOLO11 framework, the algorithm achieves performance optimization through three core innovations. Specifically, StarNet-nano is proposed as the backbone network, thereby improving the perception of features without compromising the model's lightweight architecture to improve the detection of small-scale defects. In addition, a multi-branch modulated feature pyramid network (MMFPN) is developed to optimize feature fusion while adhering to lightweight objectives, and an LSCD detection head is employed to balance model compactness with high detection accuracy. Experimental results demonstrate that SML-YOLO reduces model size by 21.1% and parameter count by 20.9%, while achieving a 2.4% improvement in detection accuracy and a 23.8% increase in detection speed. Moreover, it exhibits strong adaptability under adverse weather conditions. These findings confirm that the proposed algorithm combines lightweight design, high detection performance, and robust generalization capability. It not only provides an efficient solution for automated inspection of transmission lines, reducing manual inspection workload and minimizing maintenance downtime, but is also well-suited for deployment in resource-constrained UAV applications.