Infrastructure managers must maintain high standards to ensure user satisfaction during the lifecycle of infrastructures. Surveillance cameras and visual inspections have enabled progress in automating the detection of anomalous features and assessing the occurrence of deterioration. However, collecting damage data is typically time consuming and requires repeated inspections. The one-class damage detection approach has an advantage in that normal images can be used to optimize model parameters. Additionally, visual evaluation of heatmaps enables us to understand localized anomalous features. The authors highlight damage vision applications utilized in the robust property and localized damage explainability. First, we propose a civil-purpose application for automating one-class damage detection reproducing a fully convolutional data description (FCDD) as a baseline model. We have obtained accurate and explainable results demonstrating experimental studies on concrete damage and steel corrosion in civil engineering. Additionally, to develop a more robust application, we applied our method to another outdoor domain that contains complex and noisy backgrounds using natural disaster datasets collected using various devices. Furthermore, we propose a valuable solution of deeper FCDDs focusing on other powerful backbones to improve the performance of damage detection and implement ablation studies on disaster datasets. The key results indicate that the deeper FCDDs outperformed the baseline FCDD on datasets representing natural disaster damage caused by hurricanes, typhoons, earthquakes, and four-event disasters.

翻译：基础设施管理者必须维持高标准，以确保基础设施生命周期内的用户满意度。监控摄像头和视觉检查已推动自动检测异常特征和评估劣化发生情况的进展。然而，收集损伤数据通常耗时且需要重复检查。单类损伤检测方法的优势在于可使用正常图像优化模型参数。此外，热图的视觉评估使我们能够理解局部异常特征。作者强调了损伤视觉应用在鲁棒性和局部损伤可解释性方面的价值。首先，我们提出一种土木用途的应用，用于自动进行单类损伤检测，复现全卷积数据描述（FCDD）作为基线模型。我们通过土木工程中的混凝土损伤和钢材腐蚀的实验研究，获得了准确且可解释的结果。此外，为开发更鲁棒的应用，我们将方法应用于另一个包含复杂和噪声背景的室外领域，使用通过多种设备收集的自然灾害数据集。进一步，我们提出了一种有价值的更深层FCDD解决方案，聚焦于其他强大的骨干网络以提升损伤检测性能，并在灾害数据集上实施消融研究。关键结果表明，在代表由飓风、台风、地震及四类灾害事件造成的自然灾害损伤的数据集上，更深层FCDD优于基线FCDD。