At present, YOLO object detection algorithm is still the most mainstream method in the field of insulator defect detection, however, the existing YOLO model framework has a large number of parameters leading to the difficulty of outdoor deployment, and at the same time, the background of the insulator images taken outdoors is complex, and the defects are even more tiny, making it very difficult to be detected. To address the above problems, this paper proposes an improved insulator defect detection model YOLOv8-GCS based on the YOLOv8n object detection framework to reduce the number of parameters in the model and improve the detection precision of the model. Firstly, the C2f block in the model is replaced by a more lightweight Ghost convolution block to reduce the computational and parametric quantities of the model. Then the Coord Attention module is added at the end of the backbone network and at the second detection head to suppress the influence of the complex background on the defective parts of insulators and thus improve the detection precision of the model. At last, an SPD-Conv block is introduced so that the model of the network has no loss of important information in the process of two-fold downsampling and at the same time enhances the learning rate of the model of the network on the important features, which further improves the detection performance of the model. Analyzing the experimental results, it can be seen that the algorithm in this paper improves the mAP50 by 4% compared with the baseline model, the recall rate and the check all rate by 4.7% and 1.3%, respectively, the number of parameters is reduced by 26.7%, the size of the weight file to save the results is reduced by 1.5 MB, and the AP50 of insulator broken and pollution-flashover are improved by 4% and 8.1%, respectively.