Generative Damage Learning for Concrete Aging Detection using Auto-flight Images

Yasuno, Takato; Ishii, Akira; Fujii, Junichiro; Amakata, Masazumi; Takahashi, Yuta

doi:10.22260/isarc2020/0166

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…For example, we proposed a bridge slab anomaly detector using a U-Net generator with a patch discriminator containing AAEs and an OC-SVM [28]. Additionally, we proposed a concrete damage detection method using an auto-flight UAV based on cycleGAN and morphology analysis for computing anomaly scores [29]. We also proposed a pipeline combining VAE reconstruction with an isolation forest for detecting fallen objects on road surfaces after a preprocessing translation operation using pix2pix [30].…”

Section: Civil Application and Robustness During Natural Disastersmentioning

confidence: 99%

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

Yasuno¹,

Okano²,

Fujii³

2023

AAIML

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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.

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Section: Civil Application and Robustness During Natural Disastersmentioning

confidence: 99%

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

Yasuno¹,

Okano²,

Fujii³

2023

AAIML

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“…The authors are researching a method to objectively and quantitatively evaluate the deterioration status of the dam body surface through AI-based image recognition (Yasuno, Fujii, and Amakata, 2019) (Yasuno, Ishii, Fujii, Amakata, and Takahashi, 2020) using images captured by autonomous UAV flights. In above these research, the popouts can also be counted as the number of popouts that occur on the surface of the dam body.…”

Section: Purposementioning

confidence: 99%

Study on the Estimation of Concrete Defects Volume on Dam Body Surface

Ishii,

Sugawara,

Amakata

2023

PHMAP_CONF

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To maintain the safety and functionality of dams over the long term, it is necessary to make inspections more labor-saving and efficient using the latest technology and to improve the sophistication of inspections based on data. Although dam inspections cover a wide range of items, this study focuses on the continuous monitoring of popouts, a phenomenon of concrete deterioration occurring on the surface of a dam body. It is difficult to predict whether a popout will occur from information on the body surface of the dam, owing to the generation mechanism of the popout. The number of popouts was monitored over time; however, no examples of shape changes were monitored over time. Advancements in various digital technologies are required to accurately evaluate changes in the dam body's surface over time; therefore, in this study, three-dimensional (3D) point-cloud data is created by the Structure from Motion (SfM) from images captured by a Unmanned Aerial Vehicle (UAV) of the concrete defect area due to the popout in an arch dam in the Tohoku region of Japan. The volume of concrete defects of a popout in each of two different periods was calculated by estimating the plane shape of the surface of the dam body. In addition, the shapes of two popouts were compared to confirm the possibility of predictive signs of change.

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“…Data domain translation [ (Tsialiamanis et al, 2022b;Luleci et al, 2022c;Zhang et al, 2020;Yasuno et al, 2020;Bianchi et al, 2021)] (a total of five studies) is seen less frequently than the other applications in the literature but could be very promising and advantageous to many other problems in civil SHM. The core research problem of domain translation applications is learning the distinct mapping between the data domains.…”

Section: Frontiers In Built Environmentmentioning

confidence: 99%

“…The authors in this paper (Yasuno et al, 2020), addressed the difficulty of having paired and annotated images for supervised classification applications in SHM. In this work, the authors proposed to use CycleGAN to generate undamaged concrete images from damaged concrete images.…”

Section: Figure 14mentioning

confidence: 99%

A literature review: Generative adversarial networks for civil structural health monitoring

Luleci

Çatbaş

Avcı

2022

Front. Built Environ.

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Structural Health Monitoring (SHM) of civil structures has been constantly evolving with novel methods, advancements in data science, and more accessible technology to address issues related to structural safety, operations, and resiliency. Research and development in the civil SHM field during the last few decades have been progressive due to the increasing use of Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL). Particularly, Generative Adversarial Networks (GAN), which is a subfamily of Deep Learning has been highly favored in the SHM community within the last couple of years. After its release in 2014, GANs (original GAN and other GAN variants) have been in use for a wide variety of applications in various disciplines, and it has been one of the most popular research topics in the AI-ML domain. While there has not been a review study on the applications of GAN in the civil SHM field, this paper aims to fill this gap by presenting a literature review of the studies that employed GAN specifically in civil SHM applications from 2014 to date, in a condensed format. This study intends to inform SHM practitioners and researchers about GANs and present the highlights of the published work on GANs in the civil SHM field.

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Generative Damage Learning for Concrete Aging Detection using Auto-flight Images

Cited by 4 publications

References 12 publications

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

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

Study on the Estimation of Concrete Defects Volume on Dam Body Surface

A literature review: Generative adversarial networks for civil structural health monitoring

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