Journal ArticleDOI
Vision‐Based Automated Crack Detection for Bridge Inspection
Chul Min Yeum,Shirley J. Dyke +1 more
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TLDR
The effectiveness of this technique can be used to successfully detect cracks near bolts and the extracting of images of damage sensitive areas from different angles to increase detection of damage and decrease false-positive errors.Abstract:
The visual inspection of bridges demands long inspection time and also makes it difficult to access all areas of the bridge. This paper presents a visual-based crack detection technique for the automatic inspection of bridges. The technique collects images from an aerial camera to identify the presence of damage to the structure. The images are captured without controlling angles or positioning of cameras so there is no need for calibration. This allows the extracting of images of damage sensitive areas from different angles to increase detection of damage and decrease false-positive errors. The images can detect cracks regardless of the size or the possibility of not being visible. The effectiveness of this technique can be used to successfully detect cracks near bolts.read more
Citations
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Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks
TL;DR: This article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features, and shows quite better performances and can indeed find concrete cracks in realistic situations.
Journal ArticleDOI
Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types
TL;DR: A framework for quasi real-time damage detection on video using the trained networks is developed and the robustness of the trained Faster R-CNN is evaluated and demonstrated using 11 new 6,000 × 4,000-pixel images taken of different structures.
Journal ArticleDOI
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
TL;DR: An overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment and some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented.
Journal ArticleDOI
Deep Transfer Learning for Image-Based Structural Damage Recognition
TL;DR: This article implements the state‐of‐the‐art deep learning technologies for a civil engineering application, namely recognition of structural damage from images with four naïve baseline recognition tasks: component type identification, spalling condition check, damage level evaluation, and damage type determination.
Journal ArticleDOI
Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network
TL;DR: The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures as mentioned in this paper, however, the current manual crack description is inadequate and outdated.
References
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TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.