When images of surface defects on tubular vessels are acquired, the images are prone to change due to environmentally variable factors, resulting in inconsistency between the collected image features and the algorithm’s training image features. To solve this problem of degradation in detection accuracy, in this study, an unsupervised domain adaptive surface defect detection algorithm is proposed. First, the convolutional neural network extracts the labeled data in the source domain and unlabeled data in the target domain. Second, the strategy of adversarial training of domain classifiers is used to align image-level features and instance-level features. To fully utilize the correlation of feature maps at different scales, an improved channel-attention fusion domain classifier is proposed to enhance the discriminative ability of the domain classifiers. Finally, the results of the corresponding domain classifiers are strongly matched to ensure that the network detection results are independent of input data source. Specifically, the detection is conducted under the condition of the generated domain adaptive domain invariant to enhance the detection accuracy. The experimental results show that the detection accuracy of the algorithm model is improved from 83.1% to 93.4%, which significantly reduces the phenomenon of wrong detection and missed detection, and the algorithm is more adaptable to the variable environment of the actual production.