Insulator defect detection is a crucial task in the intelligent inspection of transmission lines. Currently, there is a shortage of image samples, and generating synthetic samples for data augmentation is an effective solution. However, synthetic samples inevitably exhibit domain distribution differences from real samples. To address this issue, a few-shot cross-domain hybrid transfer supervised domain adaptation model is proposed. This approach utilizes a large number of labeled synthetic images as the source domain and a small number of real images as the target domain, enabling effective use of synthetic samples and optimized alignment of cross-domain feature distributions, thereby improving the performance of insulator defect detection under few-shot scenarios. First, source domain images are adapted to match the target-like class distribution and are used to perform foreground-background hybrid augmentation on target domain images, improving the quality and diversity of target samples. Secondly, cross-domain style perturbation is applied to source images to further reduce the domain distribution gap with the target domain. Finally, a domain classifier based on adversarial training is employed to align cross-domain invariant features between the source and target domains, enhancing the model’s generalization ability across domains. Under the condition of using only 8 real insulator defect images for training, the proposed model achieves a 9.0% improvement in AP50 compared to the baseline detection model. Ablation experiments further evaluate the effectiveness of each module. In addition, the proposed model consistently outperforms other supervised domain adaptation approaches across different insulator defect datasets. For instance, it achieves a 3.6% AP50 improvement over the best competing model on a self-constructed insulator defect dataset, and a 2.4% AP50 improvement on the public insulator defect dataset IDID.