Abstract
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.
Abstract (translated)
基础设施管理人员必须维持高标准,以确保用户在基础设施生命周期中的满意度。监控摄像头和视觉检查已经推动了自动化异常特征检测和评估恶化趋势的进展。通常,收集损坏数据会限制时间和重复检查。一型损坏检测方法的优点在于只有正常图像才能优化参数。同时,使用热图进行视觉解释能够让我们理解局部异常特征。我们提出了一种民用应用程序,使用全卷积数据描述(FCDD)自动化一型损坏检测。我们还使用基于分卷积神经网络的增强学习激活映射(GAN)从卷积神经网络的接收域中绘制增强学习映射,以可视化损坏特征的解释。我们在实验研究中展示了它的优点和未来的工作内容:混凝土破坏和钢材腐蚀,并提到了它的实用性和未来的研究方向。
URL
https://arxiv.org/abs/2303.01732
