Deep learning is currently a mainstream method for crack detection in pavement images, but it requires a large number of manually-annotated ground-truth images for training. However, in reality, it is time-consuming and laborious to obtain manually-annotated ground-truth images. This study proposes a method for crack detection in pavement images based on an improved generative adversarial network. The study regards crack detection in pavement images as a kind of anomaly detection problem based on image cross-domain transformation and uses a fixed-point generative adversarial network to automatically convert the crack image into a one-to-one corresponding crack-free image with supervision. Then, the study differentiates the original image and the generated image, and the salient objects in the difference image correspond to the crack detection results. The test results on the public dataset CrackIT show that the method in this study can achieve accurate crack detection without relying on the manually-annotated ground-truth images. In addition, the method achieves comparable performance to supervised deep learning methods in terms of precision, recall, and F1-score.