Abstract: The integrity of insulators in transmission lines is crucial for ensuring the safety of these systems. To assess the condition of insulators, this paper presents a detection and recognition method utilizing the YOLOv8 algorithm. The model was trained on an open-source insulator dataset that is publicly accessible online. Employing the Ultralytics framework, this study utilized the YOLOv8 neural network model to train the dataset, achieving an accuracy rate (P) of 94.2% and a mean Average Precision at IoU 0.50 (mAP50) of 92.2%. Additionally, a visual interface was developed using the PyQt5 library to facilitate the selection of the training model and test images. The experimental results demonstrate that this method is both effective and efficient in detecting the integrity of transmission line insulators, yielding promising application outcomes.