Crack detection using fusion features‐based broad learning system and image processing

Zhang, Yang; Yuen, Ka‐Veng

doi:10.1111/mice.12753

Cited by 96 publications

(51 citation statements)

References 95 publications

(74 reference statements)

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“…Nowadays, machine learning (ML) and deep learning (DL) have been at the forefront of a plethora of research activities in many disciplines, where topics in structural engineering, for example, system parameter identification (Perez‐Ramirez et al., 2016), bolt loosening detection (Yang et al., n.d.), and crack detection, significantly benefit from these technologies. In order to reduce the cost and safety risk while improving detection accuracy and automation, researchers have proposed a variety of image detection technologies combining ML/DL with CV to recognize cracks (Kong et al., 2021; Liang, 2019; Miao et al., 2021; Żarski et al., 2021; Zhang & Yuen, 2021). In general, vision‐based crack detection has three main tasks: identification, localization, and segmentation.…”

Section: Background and Motivationsmentioning

confidence: 99%

Multistage semisupervised active learning framework for crack identification, segmentation, and measurement of bridges

Zheng

Gao

et al. 2022

Computer aided Civil Eng

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In bridge health monitoring (BHM), crack identification and width measurement are two of the most important indices for evaluating the functionality of bridges. In order to reduce the labor cost in field detection, researchers have proposed a variety of deep learning (DL)-based detection techniques for crack recognition. However, some problems still exist in extending these techniques to practical applications, such as data annotation difficulty, limited model generalization ability, and inaccuracy of the DL identification of the actual crack width measurement. In this paper, an application-oriented multistage crack recognition framework is proposed, namely, Convolutional Active Learning Identification-Segmentation-Measurement (CAL-ISM). It includes four steps:(1) pretraining of the benchmark classification model, (2) retraining of the semisupervised active learning model, (3) pixel-level crack segmentation, and (4) crack width measurement. Beyond numerical experiments, the performance of the CAL-ISM is validated for practical applications: (i) bridge column test specimen and (ii) field BHM projects. In conclusion, the obtained results from these applications shed light on the high potential of CAL-ISM for BHM applications, which is recommended in future deployments for BHM. BACKGROUND AND MOTIVATIONSBy the end of 2020, there are 912,800 highway bridges in China, including 6444 long-span bridges and 119,935 normal bridges. The total mileage of the highway is about 5.19 million kilometers (km), of which the maintenance mileage of the highway is 5.14 million km, accounting for 99.0% of the total mileage (Ministry of transport of the People's Republic of China, 2020). The remaining 1% is for newly constructed highways with no need for immediate © 2022 Computer-Aided Civil and Infrastructure Engineering. maintenance. Similarly, there are more than 618,000 bridges in the United States, where nearly 36% of the bridges need repair work, and 7.3% of them are considered structurally deficient (American Road and Transportation Builders Association, 2021). Therefore, surging demands in the maintenance work of highway bridges in China, the United States, and many other countries are worldwide realities and it is a necessity to develop effective methods to evaluate the service functionalities of bridge structures. Because of cost-efficiency and easy-forming, reinforced

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Section: Background and Motivationsmentioning

confidence: 99%

Multistage semisupervised active learning framework for crack identification, segmentation, and measurement of bridges

Zheng

Gao

et al. 2022

Computer aided Civil Eng

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“…They have also used over 20,000 images of 1024 × 1024 higher resolution images. Long-short term memory (LSTM) based deep learning convolutional neural networks have also been applied for crack identification [ 17 ]. This paper is unique in the sense that the use of the CNN-LSTM type method has not been presented before in literature for problems of crack identification.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows

Palevicius

Pal

Landauskas

et al. 2022

Sensors

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Deep learning-based methods, especially convolutional neural networks, have been developed to automatically process the images of concrete surfaces for crack identification tasks. Although deep learning-based methods claim very high accuracy, they often ignore the complexity of the image collection process. Real-world images are often impacted by complex illumination conditions, shadows, the randomness of crack shapes and sizes, blemishes, and concrete spall. Published literature and available shadow databases are oriented towards images taken in laboratory conditions. In this paper, we explore the complexity of image classification for concrete crack detection in the presence of demanding illumination conditions. Challenges associated with the application of deep learning-based methods for detecting concrete cracks in the presence of shadows are elaborated on in this paper. Novel shadow augmentation techniques are developed to increase the accuracy of automatic detection of concrete cracks.

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“…With the introduction of a convolutional neural network (CNN), researchers found that this type of network based on convolution, pooling, and fully connected layers allows for automated feature extraction, and many automated damage detection methods are based on this type of network 22–24 . Zhang and Yuen 25 proposed a novel crack detection system; it is a fusion features‐based broad learning system that has the advantage of efficient training without GPU acceleration. Kim et al 26 proposed a shallow CNN‐based architecture for surface concrete crack detection, and the hyperparameters of the proposed model were optimized to achieve the maximum accuracy of crack detection with the least calculation.…”

Section: Introductionmentioning

confidence: 99%

A high‐frequency feature enhancement network for the surface defect detection of welded rebar

2022

Structural Contr & Hlth

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The surface defect detection of welded rebar is an important part of construction handover inspection, which is mainly done manually. To improve the efficiency and objectivity of this work, deep learning-based defect detection methods can be used, but surface defects of welded rebar have the characteristics of insignificant pixel change and small defect areas. Therefore, this paper proposes a high-frequency feature enhanced network (HFFENet) for detecting defects on the welded surface of rebar. The proposed method uses an autoencoder as the base network and inserts a high-frequency feature enhanced module (HFFEM). The module extracts high-frequency components of low-level features by utilizing discrete cosine transform (DCT) and transforming them into feature enhanced coding, which is fused into the upsampling layers by means of concatenation. The proposed method is tested with a welded rebar surface defect dataset, and the results show that the proposed method can achieve a mIoU of 86.2%. Finally, it is experimentally demonstrated that high-frequency features are more important for defect detection tasks of small areas and more widely distributed pixel histograms.

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Crack detection using fusion features‐based broad learning system and image processing

Cited by 96 publications

References 95 publications

Multistage semisupervised active learning framework for crack identification, segmentation, and measurement of bridges

Multistage semisupervised active learning framework for crack identification, segmentation, and measurement of bridges

Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows

A high‐frequency feature enhancement network for the surface defect detection of welded rebar

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