One-class Damage Detector Using Deeper Fully Convolutional Data Descriptions for Civil Application

Abstract

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 fourevent disasters.

Imbalanced Anomaly Detection Using Augmented Deeper FCDDs for Wooden Sleeper Deterioration Prognostics

“…Additionally, the visual heat map explanation enables us to discriminate between localized defective features. The authors (Yasuno, Okano, & Fujii, 2023) found that the deeper fully-convolutional data descriptions (FCDDs) has been applicable to several damage data sets of concrete/steel components in structures: pavement, bridge, and dam, and fallen tree, and wooden building collapse in disasters: typhoon, earthquake. However, it is not yet known to feasible to railway components that includes deterioration of wooden sleepers.…”

“…The authors (Yasuno et al, 2023) have already reported the deeper FCDDs and found the applicability to damage data sets of bridge, dam, and building. However, as an unsupervised deep anomaly detection approach, the deeper FCDDs has been not yet known to feasible to video frame images in railway track that contains ballast stones, rail, spike, fastener, and concrete/wooden sleepers.…”

“…We herein present the construction of a baseline FCDD (Yasuno et al, 2023) with an initial backbone B = 0 and performed CNN27 mapping Φ 0 W (A i ) from the augmented input image A i in the dataset. We also present deeper FCDDs focusing on elaborate backbones B ∈ {VGG16, ResNet101, Inceptionv3} with a mapping operation Φ B W (A i ) to achieve more elaborately performance.…”