Insulator detection has important application value in the transmission line intelligent inspection, and insulator detection based on deep learning is a commonly used method. However, in some cases, only data of a certain type of insulator can be obtained, and if the model obtained by the training is directly applied to the detection of cross-domain insulators, its performance will decrease sharply. To solve this problem, a dual adversarial unsupervised domain adaptation insulator detection algorithm was proposed. Specifically, in order to reduce the impact of the complicated background of the insulator image on the detection performance, a confusion discrimination mechanism was designed, in which two different types of insulator images are input to two different discriminators for classification, and then the two insulators are cross-classified through adversarial training to learn domain-invariant features. In addition, the discriminator and the feature extractor were optimized respectively by the two classification results of the maximum and minimum target domains to alleviate the problem of large differences in the appearance of different types of insulators. A large number of experiments have proved the effectiveness of the proposed method.