With the expanding scale of underground cable pipelines, the stable operation of underground power grid is essential for the orderly development of human production and life. However, once there are foreign objects, defects, or other anomalies in pipelines, it will lead to a series of problems, such as electric discharge, trip, and fire, seriously threatening the life and property safety of surrounding residents. Thus the research on anomaly detection of underground cable pipelines plays an important role. To address the challenges of limited anomaly samples and complex detection, an anomaly detection method for underground cable pipelines is proposed based on reconstruction error. Prior to training, a pseudorandom anomaly generation module is employed to create random anomaly shapes and pair them with random anomaly textures. The anomaly images and corresponding detection result maps produced by this module facilitate self-supervised training and improve the generalization performance of anomaly detection, effectively addressing the dynamic nature of anomalies within the pipeline. This model primarily consists of two encoder–decoder architecture networks, with the first network incorporating the convolutional block attention module in its middle to enhance the feature extraction capability during image reconstruction. The experiments on public dataset MVTec AD and self-build dataset HHU_UP verify the effectiveness and robustness of the proposed method compared with some current existing anomaly detection methods.