SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks

Dorafshan, Sattar; Thomas, Robert J.; Maguire, Marc

doi:10.1016/j.dib.2018.11.015

Cited by 204 publications

(113 citation statements)

References 5 publications

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“…Among the 40,000 images, 20,000 images corresponded to cracked and intact images each. Another image data set provided by the Utah State University (USU) was used, the data in which were recorded uses a 16 MP Nikon camera [34]. The data consisted of 54 bridge images, 72 concrete wall images, and 104 pavement images.…”

Section: Concrete Crack Image Data For Deep Learning a Crack Imagementioning

confidence: 99%

“…To train the segmentation neural network to realize pixellevel classification, labeled images are required to identify the exact crack region in the image. The image data set provided by Dorafshan et al could be used to develop a classification algorithm [34]; however, for the purpose of the present study, labeled images were required to be prepared. To this end, the LEAR Image Annotation Tool [35] was used, as shown in Fig.…”

Section: B Labeled Imagementioning

confidence: 99%

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Multiscale and Adversarial Learning-Based Semi-Supervised Semantic Segmentation Approach for Crack Detection in Concrete Structures

et al. 2020

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Typically, the operational lifetime of underground concrete structures is several decades. At present, many such structures are approaching their original life expectancy. In this stage, the essential functionality of the structures may be considerably degraded, leading to various safety hazards such as collapse roof and tunnel flooding. In general, to overcome such problems, the maintenance of underground structures has been conducted through manual subjective inspections so far. However, recently, several objective inspection technologies have been actively developed by fusing artificial intelligence and imaging techniques recently. In particular, deep learning algorithms have been developed to detect concrete cracks, based on a large amount of data for supervised learning, including numerous labeled images. Such data acquisition requires considerable time and effort. To reduce these costs, in this study, multiscale and adversarial learning techniques were applied to realize crack detection. A total of 1,200 labeled data and 3,000 unlabeled data were used to implement and verify the proposed method. The multiscale segmentation neural network, discriminator neural network, and adversarial learning technique were used to realize accurate crack detection, enhance the learning performance, and ensure the efficiency of training data, respectively. The resulting algorithm had a pixel accuracy, mean intersection over union, frequency weighted intersection over union, and F1 score of 98.176%, 88.936%, 96.525%, and 88.789%, respectively. The proposed technique can be used to examine the conditions to ensure the safe maintenance of aging structures.

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Section: Concrete Crack Image Data For Deep Learning a Crack Imagementioning

confidence: 99%

Section: B Labeled Imagementioning

confidence: 99%

Multiscale and Adversarial Learning-Based Semi-Supervised Semantic Segmentation Approach for Crack Detection in Concrete Structures

et al. 2020

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“…Various studies have been done on vision-based crack recognition methods, especially for concrete [2], bridge, road or the surfaces of buildings, however, less for ground cracks. No matter which kinds of cracks, they are all long and narrow, as well as only distribute in a limited parts of an image, which shows a problem of imbalance related to the number of the pixels of the crack and background.…”

Section: Introductionmentioning

confidence: 99%

Ground Crack Recognition Based on Fully Convolutional Network With Multi-Scale Input

Cheng

Guo

et al. 2020

IEEE Access

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Daily inspection of ground crack over mine goaf is a necessary task for environmental protection and mining safety. However, the traditional manual inspection method is time-consuming, misjudgement-prone and with potential dangers because the goaf normally locates in remote regions and inevitably induces complicated and gullies-cross landform. Therefore, Unmanned Aerial Vehicle(UAV) is adopted to capture aerial images of the ground crack, which provides a convenient way for goaf inspection. Though aerial images display a full view for the region containing cracks, the rugged terrain results in rich noises that have the similar characteristics with cracks in an image, such as shadow, cliff, terrace and so on, which may lead to misclassification of cracks. To overcome the obstacles, an improved semantic segmentation framework called MSI-FCN is proposed in the paper. Firstly, a statistic pre-processing method is employed to remove useless patches before training, with the purpose of decreasing the training complexity. Following that, a multi-scale input method is applied to integrate the patches with the output tensor of each stage based on FCN, which precisely recognizes pixel-level crack. Furthermore, a multi-scale connection module is constructed to select the most important contextual information from the features under multiple scales and help for recovering high-dimension features. In particular, a statistic weighted softmax cross-entropy loss function is presented so as to classify crack pixels more rationally. The experimental results for three groups of images under various environmental conditions indicate that MSI-FCN is superior to the FCN-8, FCD-56 and DeepCrack-GF. In addition, the proposed method also shows high performance on CFD and deepcrack dataset. Thus, multi-scale information embedded in the proposed method can provide more local details of low-level features to high-level stages and improve the recognition performances.

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“…The number of images collected depends on a number of factors, but it is commonly in the several thousands. For instance, SDNET2018 [21], with more than 56,000 labeled images of concrete structures, covers three small lab-made bridge decks, walls of a building, and several paved sidewalks, which are significantly smaller than common inspected infrastructures in practice. Manual identification of flaws in such large image sets is time consuming and prone to inaccuracy because of inspector fatigue or human error [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

2019

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This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare different edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS.

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SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks

Cited by 204 publications

References 5 publications

Multiscale and Adversarial Learning-Based Semi-Supervised Semantic Segmentation Approach for Crack Detection in Concrete Structures

Multiscale and Adversarial Learning-Based Semi-Supervised Semantic Segmentation Approach for Crack Detection in Concrete Structures

Ground Crack Recognition Based on Fully Convolutional Network With Multi-Scale Input

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

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