Computer Vision Techniques in Construction, Operation and Maintenance Phases of Civil Assets: A Critical Review

Xu, Shuyuan; Wang, Daming; Wang, Xiangyu; Shou, Wenchi

doi:10.22260/isarc2019/0090

Cited by 13 publications

(8 citation statements)

References 59 publications

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“…(2) For concrete surface quality defects, current research focuses on discussing the accuracy of algorithms (Martinez et al, 2019;Spencer et al, 2019;Xu et al, 2019), ignoring the evaluation and decision-making of concrete surface defects and the knowledge of the field of defects has not been effectively reused. In addition, the semantic gap between the image features extracted by computer vision and the information that people obtain from the image makes the application of computer vision limited (Zhong et al, 2019a, b).…”

Section: Research Gapmentioning

confidence: 99%

Evaluation and decision-making framework for concrete surface quality based on computer vision and ontology

Chai

Wang

2022

ECAM

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PurposeDefects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.Design/methodology/approachCombining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.FindingsComputer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.Originality/valueThis paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

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Section: Research Gapmentioning

confidence: 99%

Evaluation and decision-making framework for concrete surface quality based on computer vision and ontology

Chai

Wang

2022

ECAM

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“…For example, bridge crack detection [3] using unmanned aerial vehicle (UAV), and so forth. After deep learning revolution 2014 [4], vision-base infrastructure inspection technique have researched using deep learning algorithms [5]. UAV as an autonomous robotics and vision-base deep learning technique has been combined for powerful inspection application [6] [7][8] [9].…”

Section: Related Workmentioning

confidence: 99%

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

Yasuno¹,

Ishii²,

Fujii³

et al. 2020

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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In order to health monitoring the state of largescale infrastructures, image acquisition by autonomous flight drone is efficient for stable angle and high quality image. Supervised learning requires a great deal of dataset consisting images and annotation labels. It takes long time to accumulate images including damaged region of interest (ROI). In recent years, unsupervised deep learning approach such as generative adversarial network (GAN) for anomaly detection algorithms have progressed. When a damaged image is a generator input, it tends to reverse from the damaged state to the health-like state image. Using the distance of distribution between the real damaged image and the generated reverse aging health-like image, it is possible to detect the concrete damage automatically from unsupervised learning. This paper proposes an anomaly detection method using unpaired image-to-image translation mapping from damaged image to reverse aging fake like health condition. Actually, we apply our method to field studies, and we examine the usefulness for health monitoring concrete damages.

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“…There has been over 20 datasets of surface damage for industrial products that have focused on various materials: steel, metal, aluminum, tile, fabric, printed board, solar panel, and civil infrastructures: concrete, road, pavement, bridge, and rail [5]. The construction domain is no exception, image-based structural health monitoring and visual inspection techniques have been facilitated using deep learning algorithms [6,7]. Visual structural datasets enable to promote the development of widespread applications, over 80 studies towards the infrastructure damage: deterioration, displacement, and exfoliation [8].…”

Section: Introduction 11 Related Work On Vision-based Anomaly Detectionmentioning

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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Computer Vision Techniques in Construction, Operation and Maintenance Phases of Civil Assets: A Critical Review

Cited by 13 publications

References 59 publications

Evaluation and decision-making framework for concrete surface quality based on computer vision and ontology

Evaluation and decision-making framework for concrete surface quality based on computer vision and ontology

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

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

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