Deep learning-based defect detection of transmission line insulation equipment helps enhance power grid stability and inspection personnel safety. However, existing research still falls short of meeting practical inspection requirements [ 1 ]. To address this issue, this paper proposes a novel Is-YOLO model to improve defect detection accuracy in complex aerial inspection scenarios. The designed C3k2_IDC module adopts a multi-branch parallel structure with diverse convolutional kernels, expanding the receptive field while preserving computational efficiency. The C3k2_StarsBlock module leverages star operations to capture high-dimensional features and further promote detection accuracy. In addition, a newly designed P2/4 tiny-target detection head achieves substantial improvement in small-object detection performance. Experimental results on a UAV-captured dataset of transmission line insulator defects demonstrate that the proposed Is-YOLO model outperforms YOLOv11-n with only an 11.5% increase in model parameters. Its mAP50 rises from 84.5% to 88.7%, mAP75 from 62.3% to 67.1%, and mAP50–95 from 61.4% to 65%. With moderate computational overhead and significant performance gains, Is-YOLO can serve as an efficient solution for insulator defect detection.