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

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.

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Structural crack detection using deep convolutional neural networks

Automation in Construction ◽

10.1016/j.autcon.2021.103989 ◽

2022 ◽

Vol 133 ◽

pp. 103989

Author(s):

Raza Ali ◽

Joon Huang Chuah ◽

Mohamad Sofian Abu Talip ◽

Norrima Mokhtar ◽

Muhammad Ali Shoaib

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Crack Detection ◽

Deep Convolutional Neural Networks

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Deep Convolutional Neural Networks for Road Crack Detection: Qualitative and Quantitative Comparisons

10.1109/ist50367.2021.9651375 ◽

2021 ◽

Author(s):

Jiahe Fan ◽

Mohammud Junaid Bocus ◽

Li Wang ◽

Rui Fan

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Crack Detection ◽

Deep Convolutional Neural Networks ◽

Qualitative And Quantitative

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Crack recognition automation in concrete bridges using Deep Convolutional Neural Networks

MATEC Web of Conferences ◽

10.1051/matecconf/202134903014 ◽

2021 ◽

Vol 349 ◽

pp. 03014

Author(s):

Hajar Zoubir ◽

Mustapha Rguig ◽

Mohammed Elaroussi

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Concrete Bridges ◽

Unmanned Aerial Systems ◽

Concrete Bridge ◽

Deep Convolutional Neural Networks ◽

Concrete Crack ◽

Testing Accuracy ◽

Crack Recognition ◽

Aerial Systems

Using Unmanned Aerial Systems (UASs) for bridge visual inspection automation necessitates the implementation of Deep Convolutional Neural Networks (DCNNs) to process efficiently the large amount of data collected by the UASs sensors. However, these networks require massive training datasets for the defects recognition and detection tasks. In an effort to expand existing concrete defects datasets, particularly concrete cracks in bridges, this paper proposes a public benchmark annotated image dataset containing over 6900 images of cracked and non cracked concrete bridges and culverts. The presented dataset includes some challenging surface conditions and covers concrete cracks with different sizes and patterns. The authors analyzed the proposed dataset using three state of the art DCNNs in Transfer Learning mode. The three models were used to classify the cracked and non cracked images and the best testing accuracy obtained reached 95.89%. The experimental results showcase the potential use of this dataset to train deep networks for concrete crack recognition in bridges. The dataset is publicly available at https://github.com/MCBDD-ZRE/Concrete-Bridge-Crack-Dataset- for academic purposes.

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Autonomous Bridge Crack Detection Using Deep Convolutional Neural Networks

Proceedings of the 3rd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2019) ◽

10.2991/iccia-19.2019.42 ◽

2019 ◽

Author(s):

Hongyan Xu ◽

Xiu Su ◽

Huaiyuan Xu ◽

Haotian Li

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Crack Detection ◽

Deep Convolutional Neural Networks

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Concrete crack detection and 3D mapping by integrated convolutional neural networks architecture

Advances in Structural Engineering ◽

10.1177/1369433220975574 ◽

2020 ◽

pp. 136943322097557

Author(s):

Krisada Chaiyasarn ◽

Apichat Buatik ◽

Suched Likitlersuang

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Crack Detection ◽

3D Model ◽

Detection System ◽

Crack Density ◽

Support Vector ◽

Deep Convolutional Neural Networks ◽

Data Set ◽

Softmax Classifier

This paper presents an image-based crack detection system, in which its architecture is modified to use deep convolutional neural networks in a feature extraction step and other classifiers in the classification step. In the classification step, classifiers including Support Vector machines (SVMs), Random Forest (RF) and Evolutionary Artificial Neural Network (EANN) are used as an alternative to a Softmax classifier and the performance of these classifiers are studied. The data set was created from various types of concrete structures using a standard digital camera and an unmanned aerial vehicle (UAV). The collected images are used in the crack detection system and in creating a 3D model of a sample concrete building using an image- based 3D photogrammetry technique. Then, the 3D model is used to create a mosaic image, in which the crack detection system was applied to create a global view of a crack density map. The map is then projected onto the 3D model to allow cracks to be located in the 3D world. A comparative study was conducted on the proposed crack detection system and the results prove that the combined architecture of CNN as a feature extractor and SVM as a classifier shows the best performance with the accuracy of 92.80. The results also show that the modified architecture by integrating CNN and other types of classifiers can improve a system performance, which is better than using the Softmax classifier.

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