It is important for infrastructure managers to maintain a high standard to ensure user satisfaction during a lifecycle of infrastructures. Surveillance cameras and visual inspections have enabled progress toward automating the detection of anomalous features and assessing the occurrence of the deterioration. Frequently, collecting damage data constraints time consuming and repeated inspections. One-class damage detection approach has a merit that only the normal images enables us to optimize the parameters. Simultaneously, the visual explanation using the heat map enable us to understand the localized anomalous feature. We propose a civil-purpose application to automate one-class damage detection using the fully-convolutional data description (FCDD). We also visualize the explanation of the damage feature using the up-sampling-based activation map with the Gaussian up-sampling from the receptive field of the fully convolutional network (FCN). We demonstrate it in experimental studies: concrete damage and steel corrosion and mention its usefulness and future works.

翻译：基础设施管理者需在设施全生命周期内维持高标准，以确保用户满意度。监控摄像头与视觉检查已推动异常特征自动检测及退化程度评估的进展。然而，损伤数据的采集通常受限于耗时且重复的检查过程。单类损伤检测方法的优势在于仅需正常图像即可实现参数优化，同时通过热力图的可视化解释能够定位异常特征区域。我们提出一种面向土木工程领域的自动化单类损伤检测应用，采用全卷积数据描述（FCDD）方法。通过基于高斯上采样的全卷积网络（FCN）感受野激活图，实现对损伤特征的可视化解释。实验研究（涵盖混凝土损伤与钢腐蚀）验证了该方法的有效性，并讨论了其应用前景与未来研究方向。