由于无人机航拍巡检图像中绝缘子缺陷存在背景复杂、缺陷目标较小、不同检测目标尺寸相差较大等问题，目前的目标检测算法对此类目标识别时缺陷特征容易丢失，导致精度低且误识率高。为此，提出了一种基于改进YOLOv8n的输电线路绝缘子缺陷检测方法。首先，为了解决小目标绝缘子缺陷特征丢失问题，设计了PCFocalNeXt⁃C2f模块，并用其对YOLOv8n的Backbone进行优化。其次，为了解决缺陷目标的特征在多级传输中的信息丢失或退化问题，使用支持在非相邻层次上直接交互作用的渐近金字塔网络（asymptotic feature pyramid network，AFPN），对YOLOv8n的Neck进行优化。最后，引入损失函数Sigmoid⁃CIoU，对YOLOv8n中的损失函数进行优化，并采用Sigmoid⁃CIoU NMS获取检测结果，提升模型的鲁棒性。实验结果表明，所提出的算法相较于改进前的YOLOv8n，F1值提升3.3 %，平均检测精度mAP@0.5和mAP@0.5⁃0.95分别提高了3.8 %和6.1 %，同时参数量降低了23.9 %，证明了所提出的算法具有更优越的检测性能。

Due to the complex backgrounds, small defect targets, and significant size variations among different detection objects of insulator defects in drone aerial inspection images, current object detection algorithms often lose defect features when identifying such targets, resulting in low accuracy and high misidentification rates. To address this, an improved YOLOv8n-based method for detecting insulator defects in transmission lines is proposed. Firstly, to tackle the feature loss issue of small insulator defects, a PCFocalNeXt-C2f module is designed and used to optimize the Backbone of YOLOv8n. Secondly, to mitigate feature information loss or degradation during multi-level transmission, an asymptotic feature pyramid network （AFPN） supporting direct interaction across non-adjacent layers is employed to enhance the Neck of YOLOv8n. Finally, the Sigmoid-CIoU loss function is introduced to optimize the loss function in YOLOv8n, and Sigmoid-CIoU NMS is adopted to obtain detection results, improving the model's robustness. Experimental results show that compared to the original YOLOv8n, the proposed algorithm achieves a 3.3 % increase in F1-score, with mAP@0.5 and mAP@0.5-0.95 improving by 3.8 % and 6.1%, respectively, while reducing the parameter count by 23.9 %, demonstrating superior detection performance.