Computer vision-based methods for insulator defect detection from aerial images are widely used in power inspection.To address missed and false detections caused by complex backgrounds and small target scales，a YOLO-insulator defect detection model is proposed to improve detection accuracy.First，the reparameterized convolution based on channel shuffle-one-shot aggregation （RCS-OSA ） is introduced to replace the traditional two-dimensional convolution C 2f，thus enhancing the network ’s feature extraction capability.In the neck network，the RCS-OSA module is used to replace some of the C 2f convolutions，and the squeeze-and-excitation network （SENet） is introduced to enhance the model ’s ability to capture inter-channel relationships and express overall features.Finally，to address the difficulty in detecting multiple defect regions due to their small size，a small object detection layer method is proposed.This layer contains more detailed defect information and is more conducive to defect detection.Experimental results on a self-made insulator dataset demonstrate that，compared with the baseline YOLOv 8n，the YOLO-insulator model achieves higher precision，recall，and m ean average precision，improving overall model performance.