<p>Single-stage depth detectors, represented by YOLO, operate quickly and have been widely applied in industrial inspection. However, their generalization is often limited when handling domain shift caused by significant differences in the appearance and background noise of surface defects under complex working conditions. Domain adaptation technology can enhance the adaptability of detection models. To address challenges such as intricate defects in precision components, the prevalence of small defects, and strong background noise interference, a cross-domain defect location method-based consistency conditional adversarial network is developed. Firstly, the structural characteristics of the defect features are explored to construct an image-instance multi-level adversarial network, reducing the data distribution differences between domains. Secondly, the discrimination results output from the adversarial network are analysed to establish a consistent adversarial strategy, enhancing the detection adaptability. Then, a weakly supervised localization method is studied to design an image-level predictor, which preliminarily locates the instance features of the region of interest to mitigate background noise interference. Finally, the prediction similarity between the image-level predictor and the bounding box predictor is measured, which can adaptively adjust the proportion of hard-to-align instances during training, thereby improving the robustness of the detection model in locating complex defects.</p>