Timely detection of insulator self-explosion defects is of great significance to the safe and reliable operation of transmission lines. Aiming at the problems such as insufficient detection ability of insulator self-explosion defect with small target characteristics and complex model structure of deep learning model, this paper proposes a lightweight improved YOLOv8n insulator self-explosion detection method for transmission lines. Based on the YOLOv8n network model, a small target detection module is added to capture the details of the insulator self-exploding small target and improve its detection capability. Furthermore, SIoU loss function is introduced to solve the problem that the original CIoU loss function does not consider the direction between the real box and the predicted box, and the target positioning accuracy is enhanced. Finally, channel pruning method is used to prune the improved model, remove the redundant parameters of the model, reduce the floating point operations, and reduce the calculation cost and complexity of the model. The experimental results on the constructed insulator self-explosion data set show that the average accuracy of the lightweight improved method reaches 97.1%, and its floating point operations and volume are 4.9G and 1.82MB respectively, which is only 60.5% and 29.7% of the original model, which reasonably balances the accuracy of insulator self-detonation detection and the complexity of the model. In another transmission line inspection data set, the proposed method also has good detection accuracy for other types of small targets, and has a good prospect of popularization and application.