Journal ArticleDOI
Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network
Allen Zhang,Allen Zhang,Kelvin C. P. Wang,Kelvin C. P. Wang,Baoxian Li,Enhui Yang,Xianxing Dai,Yi Peng,Yue Fei,Yang Liu,Joshua Q. Li,Cheng Chen +11 more
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TLDR
The CrackNet, an efficient architecture based on the Convolutional Neural Network, is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel‐perfect accuracy.Abstract:
The CrackNet, an efficient architecture based on the Convolutional Neural Network (CNN), is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel-perfect accuracy. Unlike the commonly used CNN, CrackNet does not have any pooling layers which downsize the outputs of previous layers. CrackNet fundamentally ensures pixel-perfect accuracy using the newly developed technique of invariant image width and height through all layers. CrackNet consists of five layers and includes more than one million parameters that are trained in the learning process. The input data of the CrackNet are feature maps generated by the feature extractor using the proposed line filters with various orientations, widths, and lengths. The output of CrackNet is the set of predicted class scores for all pixels. The hidden layers of CrackNet are convolutional layers and fully connected layers. CrackNet is trained with 1,800 3D pavement images and is then demonstrated to be successful in detecting cracks under various conditions using another set of 200 3D pavement images. The experiment using the 200 testing 3D images showed that CrackNet can achieve high Precision (90.13%), Recall (87.63%) and F-measure (88.86%) simultaneously. Compared with recently developed crack detection methods based on traditional machine learning and imaging algorithms, the CrackNet significantly outperforms the traditional approaches in terms of F-measure. Using parallel computing techniques, CrackNet is programmed to be efficiently used in conjunction with the data collection software.read more
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Journal ArticleDOI
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
TL;DR: An overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment and some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented.
Journal ArticleDOI
Deep Transfer Learning for Image-Based Structural Damage Recognition
TL;DR: This article implements the state‐of‐the‐art deep learning technologies for a civil engineering application, namely recognition of structural damage from images with four naïve baseline recognition tasks: component type identification, spalling condition check, damage level evaluation, and damage type determination.
Journal ArticleDOI
Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network
TL;DR: The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures as mentioned in this paper, however, the current manual crack description is inadequate and outdated.
Journal ArticleDOI
Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete
TL;DR: Computational times for DCNN are shorter than the most efficient edge detection algorithms, not considering the training process, and show significant promise for future adoption of DCNN methods for image-based damage detection in concrete.
Journal ArticleDOI
Automated EEG-based screening of depression using deep convolutional neural network.
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Hojjat Adeli,D. P. Subha +7 more
TL;DR: It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere, consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
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Proceedings Article
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Karen Simonyan,Andrew Zisserman +1 more
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Gradient-based learning applied to document recognition
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LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
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An introduction to ROC analysis
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