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.

中文翻译：

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地下管道重建误差辅助异常检测方法

随着地下电缆管道规模的不断扩大，地下电网的稳定运行对于人类生产生活的有序发展至关重要。但管道一旦出现异物、缺陷或其他异常现象，就会引发放电、跳闸、火灾等一系列问题，严重威胁周边居民的生命财产安全。因此地下电缆管道异常检测的研究具有重要的意义。针对异常样本有限、检测复杂的挑战，提出一种基于重构误差的地下电缆管道异常检测方法。在训练之前，采用伪随机异常生成模块来创建随机异常形状并将它们与随机异常纹理配对。该模块生成的异常图像和相应的检测结果图有利于自监督训练，提高异常检测的泛化性能，有效解决管道内异常的动态特性。该模型主要由两个编码器-解码器架构网络组成，第一个网络在中间结合了卷积块注意模块，以增强图像重建过程中的特征提取能力。在公共数据集MVTec AD和自建数据集HHU_UP上的实验验证了该方法与现有的一些异常检测方法的有效性和鲁棒性。