Journal ArticleDOI
Concrete crack detection using context‐aware deep semantic segmentation network
TLDR
A novel context‐aware deep convolutional semantic segmentation network is presented to effectively detect cracks in structural infrastructure under various conditions to segment the cracks on images with arbitrary sizes without retraining the prediction network.About:
This article is published in Computer-aided Civil and Infrastructure Engineering.The article was published on 2019-11-01. It has received 120 citations till now. The article focuses on the topics: Context (language use).read more
Citations
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Journal ArticleDOI
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review.
TL;DR: The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed.
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.
Journal ArticleDOI
Densely connected deep neural network considering connectivity of pixels for automatic crack detection
TL;DR: A novel deep learning-based method considering the connectivity of pixels for automatic pavement crack detection which has the potential to complement the current practice involving visual inspection which is costly, inefficient and time-consuming is proposed.
Journal ArticleDOI
Automatic detection method of cracks from concrete surface imagery using two‐step light gradient boosting machine
TL;DR: An automated crack detection method based on image processing using the light gradient boosting machine (LightGBM), one of the supervised machine learning methods, that can detect cracks with high accuracy and training time is shortened.
Journal ArticleDOI
Structural crack detection using deep convolutional neural networks
Stefano Anzellotti,Raza Ali,Raza Ali,Joon Huang Chuah,Mohamad Sofian Abu Talip,Norrima Mokhtar,Muhammad Ali Shoaib,Muhammad Ali Shoaib +7 more
TL;DR: A review of CNN implementation on civil structure crack detection in the perspective of image pre-processing techniques, processing hardware, software tools, datasets, network architectures, learning procedures, loss functions, and network performance.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small 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.
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.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.