Abstract: With the continued construction of transmission lines in China, the manual work on line patrol is being replaced gradually by the unmanned aerial vehicle, Insulators play important role in transmission lines, however, in view of that the accidents caused by self-bursting insulators particularly frequently occur, so identifying self-bursting insulators from aerial images is an urgent task to be solved. In the aerial images, most of the insulator data belong to lossless insulators and less data belong to the self-bursting insulators, which number is small, thus not meeting the training requirements of the recognition algorithm. In allusion to the scarcity of self-exploding insulator data in existing transmission line unmanned aerial vehicle (abbr. UAV) inspections, based on generative adversarial networks a self-exploding insulator detection model was proposed. Through adversarial training between the generator and the discriminator, the proposed model could complete the detection of self-exploding insulators by only using lossless insulator data during training. The training process of the generative adversarial network was optimized. By means of introducing a guidance network, the mode collapse problem of the generative adversarial network could be solved, and the recall rate of the self-exploding insulator detection was improved; through adding a perturbation to the input of the discriminator, the sample imbalance problem in the generative adversarial network was solved, and the accuracy of the self-exploding insulator detection was improved. The reliability of the put forward method was demonstrated through comparison experiments with other anomaly detection algorithms. The reliability of each part of the put forward method was also demonstrated by ablation experiments on each part of the model. The experimental results demonstrate that the defects in traditional generative adversarial networks can be effectively avoided by the proposed generative adversarial network model, and the efficient automatic detection of self-exploding insulators is accomplished.