Journal ArticleDOI
Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V
Yue Fei,Kelvin C. P. Wang,Allen Zhang,Cheng Chen,Joshua Q. Li,Yang Liu,Guangwei Yang,Baoxian Li +7 more
TLDR
It is shown that CrackNet-V yields better overall performance particularly in detecting fine cracks compared with CrackNet, and further reveals the advantages of deep learning techniques for automated pixel-level pavement crack detection.Abstract:
A few recent developments have demonstrated that deep-learning-based solutions can outperform traditional algorithms for automated pavement crack detection. In this paper, an efficient deep network called CrackNet-V is proposed for automated pixel-level crack detection on 3D asphalt pavement images. Compared with the original CrackNet, CrackNet-V has a deeper architecture but fewer parameters, resulting in improved accuracy and computation efficiency. Inspired by CrackNet, CrackNet-V uses invariant spatial size through all layers such that supervised learning can be conducted at pixel level. Following the VGG network, CrackNet-V uses $3\times 3$ size of filters for the first six convolutional layers and stacks several $3\times 3$ convolutional layers together for deep abstraction, resulting in reduced number of parameters and efficient feature extraction. CrackNet-V has 64113 parameters and consists of ten layers, including one pre-process layer, eight convolutional layers, and one output layer. A new activation function leaky rectified tanh is proposed in this paper for higher accuracy in detecting shallow cracks. The training of CrackNet-V was completed after 3000 iterations, which took only one day on a GeForce GTX 1080Ti device. According to the experimental results on 500 testing images, CrackNet-V achieves a high performance with a Precision of 84.31%, Recall of 90.12%, and an F-1 score of 87.12%. It is shown that CrackNet-V yields better overall performance particularly in detecting fine cracks compared with CrackNet. The efficiency of CrackNet-V further reveals the advantages of deep learning techniques for automated pixel-level pavement crack detection.read more
Citations
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Journal ArticleDOI
A review of computer vision–based structural health monitoring at local and global levels:
Chuan-Zhi Dong,F. Necati Catbas +1 more
TL;DR: A general overview of the concepts, approaches, and real-life practice of computer vision–structural health monitoring along with some relevant literature that is rapidly accumulating is presented.
Journal ArticleDOI
Automated pavement crack detection and segmentation based on two‐step convolutional neural network
Jingwei Liu,Jingwei Liu,Xu Yang,Xu Yang,Stephen L. H. Lau,Xin Wang,Sang Luo,Vincent C. S. Lee,Ding Ling +8 more
TL;DR: In this article, the information of pavement cracks is extracted from pavement crosstalk and the information is used to determine the cause of pavement cracking. But, this is difficult to be done accurately.
Journal ArticleDOI
Review of Pavement Defect Detection Methods
Wenming Cao,Liu Qifan,Zhiquan He +2 more
TL;DR: The three types of 3D data representations are compared and the corresponding performance of the deep neural networks for 3D object detection is studied and compared in three ways, classification based, object detection based and segmentation based.
Journal ArticleDOI
The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis
Yue Hou,Li Qiuhan,Li Qiuhan,Zhang Chen,Zhang Chen,Guoyang Lu,Zhoujing Ye,Chen Yihan,Chen Yihan,Linbing Wang,Cao Dandan +10 more
TL;DR: This review summarizes the state-of-the-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.
Journal ArticleDOI
A cost effective solution for pavement crack inspection using cameras and deep neural networks
Qipei Mei,Mustafa Gül +1 more
TL;DR: A novel method called ConnCrack combining conditional Wasserstein generative adversarial network and connectivity maps is proposed for road crack detection, which achieves state-of-the-art performance compared with other existing methods in terms of precision, recall and F1 score.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;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.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;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.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
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.