Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring

By Billie F. Spencer Jr., Vedhus Hoskere, Yasutaka Narazaki. Engineering, 2019

1,033 citations recorded by the source corpus.

Abstract Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering is presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges.

Subfields: Civil and Structural Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/4d3b047f645c68fd7f4bacf189964d4b5356f691

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Citations in the corpus

Machine learning paradigm for structural health monitoring (2020, methodology): “A 39–45 Except for SHM and damage detection, these CV-based and CV-assisted techniques have been also applied in change detection (e.g. ground remote sensing 46 and tunnel lining) 47 .”
Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning (2021, background): “In order to address the drawbacks of manual inspection, vision-based assessment and monitoring of civil infrastructures are gaining ground [7].”
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022, background): “Damage detection refers to the detection of visual defects, such as steel corrosion, concrete cracks/delimitation, asphalt cracks, or fatigue cracks, and structural component recognition refers to the process of detecting and classifying different structure characteristics [133].”
A review of computer vision–based structural health monitoring at local and global levels (2020, background): “Spencer et al.(8) summarized the advances of CV-based applications of structural inspection and monitoring.”
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review (2020, methodology): “Most of the efforts in computer vision are concentrated in developing end-to-end learning algorithmswhile using artificial intelligence, particularly through DCNNs that was capable of achieving more than 95% of accuracy on image-based classification problems [85].”
Extraction of mechanical properties of materials through deep learning from instrumented indentation (2020, neutral): “In addition to well-known applications of machine-learning (ML) algorithms in such fields as image/video analysis (26, 27) and natural language processing (28), ML has also been used for various engineering problems, such as in the discovery of new materials (29) and in health care (30).”