Addressing the key challenges of insulator fault detection in drone-based power inspection scenarios, such as high missed detection rate for small targets, significant interference from complex backgrounds, and insufficient real-time performance, this study proposes an improved YOLOv10n detection model based on multi-scale feature collaborative optimization. By constructing a lightweight adaptive feature extraction network and a hierarchical fusion mechanism of multi-scale semantic enhancement architecture, dynamic deformable grouped convolution and channel recalibration strategies are adopted in the shallow network to enhance the sensitivity to micro-defect features, while a multi-branch dilated convolution pyramid and cross-dimensional attention mechanism are established in the deep network to build cross-scale associations, achieving a collaborative optimization of detection accuracy and computational efficiency. A shape-sensitive InSh-IoU loss function is proposed, which dynamically adjusts the weight coefficient of the bounding box shape to reduce the positioning error of targets with abnormal aspect ratios, enabling more accurate localization of insulators. Verified by a self-built insulator fault dataset, this model maintains real-time detection speed while achieving an average detection accuracy (mAP@0.5) of 97.12%, an improvement of 2.82% over the baseline model.