Improved Crack Detection and Recognition Based on Convolutional Neural Network

Chen, Keqin; Yadav, Amit; Khan, Asif; Meng, Yixin; Zhu, Kun

doi:10.1155/2019/8796743

Cited by 27 publications

(21 citation statements)

References 13 publications

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“…e false detection was tracked, and the corresponding images are shown in Figure 8, classified into three main categories, including images with cracks in the corners, images with low resolution, and images with too small cracks, respectively. In these cases, the CNN model could not perform the recognition task well because the contrast between the cracks and the background is poor [23].…”

Section: Discussionmentioning

confidence: 99%

“…As an example, Olivera and Correia [22] have developed an automatic crack detection based on the DL technique for assessing the damage in the Portuguese road system. In addition, Chen et al [23] have improved the recognition of cracks in images using a CNN model. Besides, Nhat-Duc and Nguyen Quoc-Lam [24] have proposed a classification model using Support Vector Machine for the detection of cracks on asphalt pavement.…”

Section: Introductionmentioning

confidence: 99%

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Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces

Nguyen

2021

Applied Computational Intelligence and Soft Computing

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This paper is devoted to the development of a deep learning- (DL-) based model to detect crack fractures on concrete surfaces. The developed model for the classification of images was based on a DL Convolutional Neural Network (CNN). To train and validate the CNN model, a database containing 40,000 images of concrete surfaces (with and without cracks) was collected from the available literature. Several conditions on the concrete surfaces were taken into account such as illumination and surface finish (i.e., exposed, plastering, and paint). Various error measurement criteria such as accuracy, precision, recall, specificity, and F1-score were employed for accessing the quality of the developed model. Results showed that for the training dataset (50% of the database), the precision, recall, specificity, F1-score, and accuracy were 99.5%, 99.8%, 99.5%, 99.7%, and 99.7%, respectively. On the other hand, for the validating dataset, the precision, recall, specificity, F1-score, and accuracy are 96.5%, 98.8%, 96.6%, 97.7%, and 97.7%, respectively. Thus, the developed CNN model may be considered valid because it performs the classification of cracks well using the testing data. It is also confirmed that the developed DL-based model was robust and efficient, as it can take into account different conditions on the concrete surfaces. The CNN model developed in this study was compared with other works in the literature, showing that the CNN model could improve the accuracy of image classification, in comparison with previously published results. Finally, in further work, such model could be combined with Unmanned Aerial Vehicles (UAVs) to increase the productivity of concrete infrastructure inspection.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces

Nguyen

2021

Applied Computational Intelligence and Soft Computing

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“…e second model achieves more than 99% training accuracy in the second epoch, while the first model achieves more than 99% training accuracy after eight epochs, and the third model achieves more than 99% training accuracy after the fifth epoch. Hence, BN has performed well in improving the accuracy of CNN network models for detecting concrete cracks [4,9,54].…”

Section: E Effect Of Batch Normalization (Bn) On the Training Speed And Accuracy In Concrete Crack Detection Modelsmentioning

confidence: 99%

Exploring the Detection Accuracy of Concrete Cracks Using Various CNN Models

Mohammed

Han

2021

Advances in Materials Science and Engineering

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Automatic crack detection with the least amount of workforce has become a crucial task in the inspection and evaluation of the performances of concrete structure in civil engineering. Recently, although many concrete crack detection models based on convolutional neural networks (CNNs) have been developed, the accuracy of the proposed models varies. Up-to-date, the issue regarding the convolutional neural network architecture with best performance for detecting concrete cracks is still debated in many previous studies. In this paper, we choose three established open-source CNN models (Model1, Model2, and Model3) which have been well-illustrated and verified in previous studies and test them for the purpose of crack detection of concrete structures. The chosen three models are trained using a concrete crack dataset containing 40,000 images those with 227 × 227-pixel in size. The performance of three different convolutional neural network (CNN) models was then evaluated. The comprehensive comparison result indicates that Model2 which used batch normalization is capable of the best performance amongst the three models as selected for concrete cracks detection, with recording the highest classification accuracy and low loss. In a conclusion, we recommend Model2 for a concrete crack detection task.

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“…Most of the studies related to the detection of cracks using neural networks were carried out using quite sophisticated equipment and techniques: ultrasonic signals, laser scanners, thermography, X-rays, etc [2][3][4][5][6]. Also it should be noted that in most studies the crack is monitored on a static specimen that is not subjected to cyclic loading [7,8].…”

Section: Problem Statementmentioning

confidence: 99%

Applications of neural networks for crack initiation and propagation monitoring in aircraft structures

Бурау¹,

Юцкевич²,

Kompanets³

2021

aktt

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Timely detection of fatigue cracks on aircraft structural elements is the main task in damage tolerance principle approach. In this regard, much attention in aviation is paid to the methods of non-destructive testing which requires special equipment with the involvement of highly qualified personnel. Nowadays we can see that technologies that can learn to identify defects are preferred to simplify the gap process and minimize human factor errors. A self-learning technology is incorporated in the crack detection program. This makes it possible to increase the sensitivity of defects in the mode of the used technically false equipment. Unlike the detection methods of other machine learning detection systems, the system developed in this paper can also measure the cracks without the use of sophisticated sensors. However, the proposed system requires a photo-capturing device. Compared to similar visual systems, the developed system can work with very noisy images and detect cracks up to 0.3 mm. To do this, the webcam from the mid-range segment with 1920×1080 resolutions is used, that makes such technology easy to access. All modifications in the design of the camera scheme were associated with a change in the focal length, implemented by shifting the lens relative to the matrix. It allows the camera to focus on close distance less than 50 cm. For the fatigue tests compact specimens of duralumin alloy D16T with edge stress concentrator were used. The specimens were cycle tested by cantilever banding with stress ratio R=-1. Loading bogie apply force to specimens in direction normal to specimen surface. A loading value depends on the length of the loading crank and can be adjusted if needed. To measure cracks in the processed images, a visual control program on a convolutional neural network and a sliding window algorithm were used. About 4,000 images were used to train the algorithm. The sliding window algorithm analyzes small images sequentially. One by one, image regions were selected and monitored for cracks using a convolutional neural network. Areas with detected cracks are memorized by the sliding window algorithm.

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Improved Crack Detection and Recognition Based on Convolutional Neural Network

Cited by 27 publications

References 13 publications

Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces

Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces

Exploring the Detection Accuracy of Concrete Cracks Using Various CNN Models

Applications of neural networks for crack initiation and propagation monitoring in aircraft structures

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