To address the challenges of difficulty in capturing small defects in complex backgrounds and a large number of model parameters in insulator defect detection from aerial images, we proposed an UAV insulator defect detection method based on improved GCP-YOLOv8s. First, GSConv was incorporated into the network to replace conventional convolutions, reducing the model's parameter count. Second, the Bottleneck module in C2f was replaced by the FasterNet Block module, creating a lightweight C2f-Faster module that further minimized model size. To improve the network's feature extraction capability, the efficient multi-scale attention (EMA) was integrated into the C2f-Faster forward network, forming the CF-EMA lightweight feature extraction module, which effectively addressing the challenge of extracting small defect features in complex backgrounds. Finally, to prevent the loss of minor defect feature information, additional minor defect detection layers were added to improve the fusion of shallow and deep feature maps, enhancing the detection accuracy for small defects. The experimental results demonstrate that GCP-YOLOv8s achieves an mAP@0.5 of 97.6%, marking an improvement of 1.8 percentage points over YOLOv8s, with a parameter count of only 7.2×10⁶, representing a 36.3% reduction compared to YOLOv8s. The proposed method demonstrates an effective balance between detection accuracy and model lightweight.