Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning

An, Qing; Chen, Xijiang; Du, Xixun; Yang, Jiewen; Wu, Shusen; Ban, Ya

doi:10.1155/2021/3159968

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Therefore, recent works use image processing based on convolutional neural networks (Vignesh et al, 2021), (Xu et al, 2019), (Vashpanov et al, 2019), (Li et al, 2020), (Li et al, 2021). (An et al, 2021) use a combination of a convolutional neural network and cluster segmentation. In the end, they calculate the mean crack width based on the crack area and length, derived by the half perimeter.…”

Section: The Current Situation Regarding Crack Detection and Localiza...mentioning

confidence: 99%

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

Merkle,

Solass,

Schmitt

et al. 2023

ITcon

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Bridge inspection is a time-consuming, expensive, but indispensable task. In this work, a new semi-automatic workflow for a concrete bridge condition assessment system is developed and discussed. The workflow consists of three main parts merged in the new methodology. The elements are the data acquisition with cameras, the automated damage detection and localization using a neural network, and the resulting engineering condition assessment. Furthermore, a CAD model serves as a base for the later calculations for the condition assessment. Camera images are used for both sub-millimeter crack detection using semantic segmentation by an artificial neural network and a crack localization based on a combination of a photogrammetric workflow including structure from motion (SfM) and the projection as imprinted points directly onto the as-planned CAD mesh. Moreover, an approach for crack width derivation is given. The captured crack width, crack position, and the date of detection represent the input values for subsequent crack monitoring. Thereby, this new concept is proposed as an essential step towards a time-efficient and objective life-cycle assessment of reinforced concrete structures.

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Section: The Current Situation Regarding Crack Detection and Localiza...mentioning

confidence: 99%

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

Merkle,

Solass,

Schmitt

et al. 2023

ITcon

View full text Add to dashboard Cite

show abstract

“…In recent years, many high-performance deep convolutional neural network models based on the large universal raw dataset ImageNet [18], such as AlexNet [19], VGGNet [20], ResNet [21], and DenseNet [22], have been developed, and their classification performance is sufficient to match humans [23]. Benefiting from these deep convolutional neural network models, many crack classification algorithms [24][25][26][27][28] based on deep convolutional neural networks have been proposed and successfully applied in various fields [29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

A Novel Road Crack Detection Technology Based on Deep Dictionary Learning and Encoding Networks

Fan,

Zou

2023

Applied Sciences

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Road crack detection is an important indicator of road detection. In real life, it is very meaningful work to detect road cracks. With the rapid development of science and technology, especially computer science and technology, quite a lot of methods have been applied to crack detection. Traditional detection methods rely on manual identification, which is inefficient and prone to errors. In addition, the commonly used image processing methods are affected by many factors, such as illumination, road stains, etc., so the results are unstable. In the research on pavement crack detection, many research studies mainly focus on the recognition and classification of cracks, lacking the analysis of the specific characteristics of cracks, and the feature values of cracks cannot be measured. Starting from the deep learning method in computer science and technology, this paper proposes a road crack detection technology based on deep learning. It relies on a new deep dictionary learning and encoding network DDLCN, establishes a new activation function MeLU, and adopts a new differentiable calculation method. The technology relies on the traditional Mask-RCNN algorithm and is implemented after improvement. In the comparison of evaluation indicators, the values of recall, precision, and F1-score reflect certain superiority. Experiments show that the proposed method has good implementability and performance in road crack detection and crack feature measurement.

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Automatic Container Recognition and Positioning Method Based on Hough Transform and Mask RCNN

Shao

Zhou

et al. 2022

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)

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Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning

Cited by 5 publications

References 23 publications

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

A Novel Road Crack Detection Technology Based on Deep Dictionary Learning and Encoding Networks

Automatic Container Recognition and Positioning Method Based on Hough Transform and Mask RCNN

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