Deep convolution neural network-based transfer learning method for civil infrastructure crack detection

Yang, Qiang; Shi, Weimin; Chen, Juan; Lin, Weiguo

doi:10.1016/j.autcon.2020.103199

Cited by 136 publications

(45 citation statements)

References 23 publications

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“…Therefore, the performance of the convolutional neural network is directly related to the size of the datasets. We randomly selected 14,000 images from CCIC, 18,000 images from SDNET, and 2000 images from OCD to combine into a larger crack detection dataset [46], called the Bridge Surface Crack Dataset (BSCD). Image samples from this database, made up of several different datasets, are shown in Table 1.…”

Section: A Datasetmentioning

confidence: 99%

A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure

Wang

Shao

et al. 2021

IEEE Access

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Bridge crack detection is essential to ensure bridge safety. The introduction of deep learning technology has made it possible to detect bridge cracks automatically and accurately. In this study, the Inception-Resnet-v2 algorithm was systematically improved and applied to the real-time detection of bridge cracks. We propose an end-to-end bridge crack detection model based on a convolutional neural network. This model combines the advantages of Inception convolution and residual networks, broadening the network width and alleviating the training problem of the deep network. The calculation speed is improved while still ensuring accuracy. Multi-scale feature fusion enables the network to extract contextual information of different scales, which improves the accuracy of crack recognition. The GKA (K-means clustering method based on a genetic algorithm) realizes the accurate segmentation of the target area, greatly enhances the clustering effect, and effectively improves the detection speed. In this model, large fracture datasets are used for training and testing without pre-training. The experimental results show that the performance of this method was improved in all aspects: accuracy, 99.24%; recall, 99.03%; F-measure, 98.79%; and FPS(Frames Per Second), 196. INDEX TERMS bridge crack detection, Inception-Resnet-v2, multiscale feature fusion, GKAThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Section: A Datasetmentioning

confidence: 99%

A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure

Wang

Shao

et al. 2021

IEEE Access

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“…Liu et al [20] proposed a deep learning algorithm to detect the rebar hyperbolas automatically, and the rebar depth is estimated with a high accuracy by migration of rebar hyperbola. A method based on deep learning was developed to detect concrete bugholes [21], [22], concrete cracks [23]- [27], road cracks [6], [28]- [30] and other defects [31]- [33]. When only one defect type is detected, these methods maybe achieve outstanding performance in realistic situations.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Crack and Bughole Detection for Concrete Surface Image Based on Deep Learning

et al. 2021

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Cracks and bugholes (surface air voids) are common factors that affect the quality of concrete surfaces, so it is necessary to detect them on concrete surfaces. To improve the accuracy and efficiency of the detection, this research implements a novel deep learning technique based on DeepLabv3+ to detect cracks and bugholes on concrete surfaces. Firstly, in the decoder, the 3 × 3 convolution of the feature fusion part is improved to a 3-layer depth separable convolution to reduce the information loss during upsampling. Secondly, the original expansion rate combination is changed from 1, 6, 12, 18 to 1, 2, 4, 8 to improve the segmentation effect of the model on the image. Thirdly, a weight value is added to each channel of the Atrous Spatial Pyramid Polling (ASSP) module, and the feature maps that contribute significantly to the target prediction are learned and screened. To use this method, a database is built containing 16,662 256 × 256 pixel images of bugholes and cracks on concrete surfaces. The two defects included in those images are labeled manually. The DeepLabv3+ architecture is then modified, trained, validated and tested using this database. A strategy of model-based transfer learning is applied to optimize and accelerate the learning efficiency of the model. The weights and biases of the Xception part of the model are initialized by the pretrained backbones. The results are 97.63% (crack), 93.53% (bughole) Average Precision (AP), 95.58% Mean Average Precision (MAP) and 81.87% Mean Intersection over Union (MIoU). A comparative study is conducted to verify the performance of the proposed method, and the results demonstrate that the proposed approach performs significantly better in crack and bughole detection on concrete surfaces.

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“…Wei et al [19] proposed a fastener defect detection approach on a railway track using CNN, especially the VGG16 model because it can simplify the classification procedure compared to the conventional image processing technique. In addition, Yang et al [20] utilized the VGG16 model as a base model for their study. They proposed a transfer learning method based on a deep CNN for civil infrastructure crack detection.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies [15][16][17][18][19][20][21] focused primarily on increasing the performance of crack detection algorithms. In addition, a number of studies have used cameras installed in UAVs to capture visual data only [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

Choi

Bell

Kim

et al. 2021

Sensors

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Structural cracks are a vital feature in evaluating the health of aging structures. Inspectors regularly monitor structures’ health using visual information because early detection of cracks on highly trafficked structures is critical for maintaining the public’s safety. In this work, a framework for detecting cracks along with their locations is proposed. Image data provided by an unmanned aerial vehicle (UAV) is stitched using image processing techniques to overcome limitations in the resolution of cameras. This stitched image is analyzed to identify cracks using a deep learning model that makes judgements regarding the presence of cracks in the image. Moreover, cracks’ locations are determined using data from UAV sensors. To validate the system, cracks forming on an actual building are captured by a UAV, and these images are analyzed to detect and locate cracks. The proposed framework is proven as an effective way to detect cracks and to represent the cracks’ locations.

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Deep convolution neural network-based transfer learning method for civil infrastructure crack detection

Cited by 136 publications

References 23 publications

A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure

A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure

Autonomous Crack and Bughole Detection for Concrete Surface Image Based on Deep Learning

UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

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