Recovering compressed images for automatic crack segmentation using generative models

Huang, Yong; Zhang, Haoyu; Li, Hui; Wu, S. C.

doi:10.1016/j.ymssp.2020.107061

Cited by 27 publications

(31 citation statements)

References 54 publications

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“…In the civil SHM literature, there are also others [ (Wang et al, 2019;Rastin et al, 2021;Yuan et al, 2021;Yang et al, 2022;Huang et al, 2020;Sathya et al, 2020;Sun et al, 2022;Dunphy et al, 2022)] (a total of 10 studies) who are observed that do not fit a category and address different problems. It is observed that the subject of "damage detection after increased resolution" has one study (Sathya et al, 2020) in the literature where the researchers use SRGAN to increase the resolution of the images to improve the performance of the damage classifier.…”

Section: Frontiers In Built Environmentmentioning

confidence: 99%

“…There is one study on the topic of "track irregularity estimation" on acceleration data (Yuan et al, 2021), one study on the topic of "damage identification" via acceleration data (Rastin et al, 2021), and three studies on the topic of "Annotation reduction via transfer learning" [ (Huang et al, 2020); Dunphy et al, 2022)] which aim to reduce the need of annotating data through transfer-learning are other instances of GAN applications in civil SHM.…”

Section: Frontiers In Built Environmentmentioning

confidence: 99%

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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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Section: Frontiers In Built Environmentmentioning

confidence: 99%

Section: Frontiers In Built Environmentmentioning

confidence: 99%

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

Luleci

Çatbaş

Avcı

2022

Front. Built Environ.

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“…Generative adversarial networks (GANs; Goodfellow et al., 2014) are a kind of probability‐based unsupervised generative models to learn complex real‐world distributions based on game theory and have been widely applied to solve engineering problems (Gao et al., 2019; Huang et al., 2020). Recently, continuing breakthroughs in image generation have been made by GANs.…”

Section: Introductionmentioning

confidence: 99%

A self‐sparse generative adversarial network for autonomous early‐stage design of architectural sketches

Qian

2021

Computer aided Civil Eng

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This study develops an autonomous design method for architectural shape sketches by a novel self‐sparse generative adversarial network (self‐sparse GAN), thereby overcoming the problems regarding excessive reliance on sufficient aesthetic knowledge and excessive time consumption in traditional human design. First, a new architectural shape dataset denoted “Sketch” is built by using the eXtended difference‐of‐Gaussians operator. Second, a self‐adaptive sparse transform module (SASTM) is designed following each deconvolution layer of the proposed self‐sparse GAN to utilize the sparsity of sketch images by the sparsity decomposition and feature‐map recombination. Third, the Frechet inception distance (FID) is adopted to evaluate the quality of the generated sketches by comparing the distribution of the real and generated datasets. Finally, two common image generation approaches, Wasserstein GAN with gradient penalty and self‐attention GAN, are compared with the proposed self‐sparse GAN, and results show the proposed method achieves the best performance with a relative decrease in the FID score of 11.87%. The proposed autonomous design method can give tens of thousands of sketches for a class of buildings in a few seconds using the trained network, which can help architects to choose the architectural form and/or inspire architects to consider unique schemes in the early stages of design.

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“…This method is not ideal for the detection of very fine cracks, nor can it quantify the number and types of road cracks. Huang et al [20] used compressed sensing with a generative model to decompress images for crack segmentation tasks. Compared with the traditional compressed sensing method, the calculation cost of this method is greatly reduced.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Pavement Crack Segmentation Using Pyramid Attention Network

Wang

2020

IEEE Access

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Cracks are the most common road pavement damage. Due to the propagation of cracks, the detection of early cracks has great practical significance. Traditional manual crack detection is extremely time-consuming and labor-intensive. Researchers have turned their attention to automated crack detection. Although automated crack detection has been extensively researched over the past decades, it is still a challenging task due to the intensity inhomogeneity of cracks and complexity of the pavement environment, e.g. To solve these problems, we propose an efficient pavement crack segmentation model based on deep learning. The model uses pre-trained DenseNet121 as an encoder to extract pavement features. Feature Pyramid Attention module fuses features under different pyramid scales and provides precise pixel-attention. The Global Attention Upsample module which is a combination of convolutional neural network and pyramid module acts as a decoder. The sum of Cross-entropy loss and Dice loss is selected as loss function. We use poly policy to tune learning rate. In order to verify the effectiveness of the proposed method, we conduct training and testing on the Crack500 dataset and MCD dataset. Our method achieves a Dice coefficient of 0.7681, an IoU of 0.6235 on the Crack500 dataset and 0.6909, 0.5278 on the MCD dataset. We perform ablation study to verify the effectiveness of the loss function on improving the performance of our model.

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Recovering compressed images for automatic crack segmentation using generative models

Cited by 27 publications

References 54 publications

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

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

A self‐sparse generative adversarial network for autonomous early‐stage design of architectural sketches

Convolutional Neural Network-Based Pavement Crack Segmentation Using Pyramid Attention Network

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