Automated Road Crack Detection Using Deep Convolutional Neural Networks

2018 ◽  
Author(s):  
Vishal Mandal ◽  
Lan Uong ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Crack Detection ◽  
Deep Convolutional Neural Networks

scholarly journals Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

Coatings ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 152 ◽  
Author(s):  
Zhun Fan ◽  
Chong Li ◽  
Ying Chen ◽  
Paola Di Mascio ◽  
Xiaopeng Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Crack Detection ◽  
The Other ◽  
Detection Methods ◽  
Deep Convolutional Neural Networks ◽  
Predicted Probability ◽  
Low Efficiency ◽  
The Individual ◽  
Pavement Crack Detection

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.

scholarly journals Structural crack detection using deep convolutional neural networks

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

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

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.

Eggshell crack detection using deep convolutional neural networks

2022 ◽  
Vol 315 ◽  
pp. 110798
Author(s):  
Bhavya Botta ◽  
Sai Swaroop Reddy Gattam ◽  
Ashis Kumar Datta
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Crack Detection ◽  
Deep Convolutional Neural Networks
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