Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network
01 Oct 2017-Computer-aided Civil and Infrastructure Engineering (John Wiley & Sons, Ltd)-Vol. 32, Iss: 10, pp 805-819
TL;DR: 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.
Citations
More filters
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.
500 citations
Cites background from "Automated Pixel-Level Pavement Crac..."
...[126] proposed CrackNet, an efficient architecture for the semantic segmentation of pavement cracks....
[...]
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.
Abstract: This article implements the state‐of‐the‐art deep learning technologies for a civil engineering application, namely recognition of structural damage from images. Inspired by ImageNet Chall...
460 citations
Cites methods from "Automated Pixel-Level Pavement Crac..."
...…verified experimentally by monitoring a steel frame demonstrating a high performance level for real-time SHM and structural damage detection processes; Zhang et al. (2017) designed a new CNN architecture, namely CrackNet, for pavement crack detection in pixel-level; and Vetrivel et al. (2017)…...
[...]
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.
Abstract: The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures However, the current manual crack description
421 citations
Cites methods from "Automated Pixel-Level Pavement Crac..."
...Compared with 9 days for training CrackNet (Zhang et al. 2017) after 700 iterations, FCN here only took less than an hour for 1,000 iterations....
[...]
...To compare FCN with existing state-of-the-art DNN for crack recognition, authors collected gray images to examine the performances of Pixel-SVM (Marques and Correia, 2012), CrackNet (Zhang et al. 2017), and the framework proposed in this article....
[...]
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.
401 citations
Cites methods from "Automated Pixel-Level Pavement Crac..."
...DCNNs have been used in vision-based structural health monitoring in recent years for crack detection 197 [42], road pavement cracks [55, 56], corrosion detection [57, 58], multi-damage detection [41, 59] structural 198 health monitoring [62]....
[...]
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.
389 citations
References
More filters
Proceedings Article•
03 Dec 2012TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Abstract: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
73,978 citations
Proceedings Article•
01 Jan 2015TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.
49,914 citations
01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.
42,067 citations
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
40,826 citations
"Automated Pixel-Level Pavement Crac..." refers methods in this paper
...With extracted features, the SVM classifier using Radial Basis Function (RBF) is trained via the LIBSVM program (Chang and Lin, 2011)....
[...]
TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.
17,017 citations
"Automated Pixel-Level Pavement Crac..." refers methods in this paper
...Table 3 lists the overall Precisions, Recalls, and F-measures achieved by the CrackNet, 3D shadow modeling, and Pixel-SVM....
[...]
...Precision and Recall frequently conflict with each other....
[...]
...According to Figure 9 and Table 3, the Pixel-SVM produces low Precisions but high Recalls....
[...]
...Precision and Recall are two commonly used indicators for evaluating crack detecting algorithms (Fawcett, 2006; Zhang et al., 2016a, 2017)....
[...]
...Therefore, it is challenging to achieve high Precision and high Recall simultaneously....
[...]