<p>To address the difficulty in distinguishing different types of insulators in transmission lines and the need for intelligent identification of insulator defects, a real-time detection method for intelligent classification and defect identification of transmission line insulators based on a LightWeight-YOLOv8n network is proposed. This method is used to efficiently detect isolator defects in complex environments and to quickly identify isolator classes. The LightWeight network model MobileNetV3 is used as the backbone network and is based on the YOLOv8n model. The feature extraction ability of the model is enhanced, the redundancy of model parameters is reduced, and the detection speed is improved by adding the SimAM attention mechanism at the end of the backbone network. The LightWeight CSPPC module is used to enhance the feature fusion component of YOLOv8n, reducing the computation load and network complexity while maintaining the accuracy of the model. To further improve the performance of the algorithm, the WIoUv3 bounding box regression loss function is used to replace the original CIoU loss function, and the SlideLoss is used to replace ClsLoss as the category loss function. The experimental results show that the detection accuracy reaches 92.4%, the recall rate reaches 86.6%, and the mAP50 reaches 90.6%. Meanwhile, the training speed is increased by 40.54%, floating-point operation is reduced by 28.57%, and the model parameters are reduced by 34%. Heatmap visualization analysis also showed that the improved models exhibited greater concentration and confidence than the baseline models. Compared to other algorithms, LightWeight-YOLOv8n showed significant advantages in overall performance, accuracy, and real-time target detection. After a random test of 100 images from the test set, the LightWeight-YOLOv8n model exhibited the lowest detection speed and was able to achieve real-time detection.</p>