Abstract: Irregular power defects on outer surface of power equipment have the characteristics of inconspicuous features and changeable shapes. Conventional image recognition algorithms show insufficient feature extraction ability and poor generalization. In this paper, a model for detecting the power irregular defects on outer surface of power equipment based on domain adaptation was proposed. Firstly, a domain adaptation architecture including feature generator and classifier is constructed to enhance the generalization ability of the model. Secondly, the ability of feature generator to extract texture information is enhanced by adding texture extraction branches and auxiliary loss branches. Finally, a high-precision detection model is obtained by the adversarial learning between generator and classifier. The experiment results show that the method proposed in this paper can still maintain high recognition accuracy in complex environments. The index of the intersection over union of oil leakage and corrosion defects can reach 89% and 85%, respectively. The model proposed in this paper can provide reference for equipment defect detection.